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Clean and split data sets to train and test. + - Create several representative models. + - Evaluate the performance of the model to select the best model. + - Support the entire process of developing a binary classification model. + +The name `alookr` comes from `looking at the analytics process` in the data analysis process. + +## Install alookr + +The released version is available on CRAN. but not yet. + +```{r eval = FALSE} +install.packages("alookr") +``` + +Or you can get the development version without vignettes from GitHub: + +```{r eval = FALSE} +devtools::install_github("choonghyunryu/alookr") +``` + +Or you can get the development version with vignettes from GitHub: + +```{r eval = FALSE} +install.packages(c("ISLR", "spelling", "mlbench")) +devtools::install_github("choonghyunryu/alookr", build_vignettes = TRUE) +``` + +## Usage + +alookr includes several vignette files, which we use throughout the documentation. + +Provided vignettes is as follows. + +* Cleansing the dataset +* Split the data into a train set and a test set +* Modeling and Evaluate, Predict + +```{r vignettes, eval=FALSE} +browseVignettes(package = "alookr") +``` + +## Cleansing the dataset +### Data: create example dataset + +To illustrate basic use of the alookr package, create the `data_exam` with sample function. The `data_exam` dataset include 5 variables. + +variables are as follows.: + +* `id` : character +* `year`: character +* `count`: numeric +* `alpha` : character +* `flag` : character + +```{r create_data} +# create sample dataset +set.seed(123L) +id <- sapply(1:1000, function(x) + paste(c(sample(letters, 5), x), collapse = "")) + +year <- "2018" + +set.seed(123L) +count <- sample(1:10, size = 1000, replace = TRUE) + +set.seed(123L) +alpha <- sample(letters, size = 1000, replace = TRUE) + +set.seed(123L) +flag <- sample(c("Y", "N"), size = 1000, prob = c(0.1, 0.9), replace = TRUE) + +data_exam <- data.frame(id, year, count, alpha, flag, stringsAsFactors = FALSE) + +# structure of dataset +str(data_exam) + +# summary of dataset +summary(data_exam) +``` + +### Clean dataset +`cleanse()` cleans up the dataset before fitting the classification model. + +The function of cleanse() is as follows.: + +* remove variables whose unique value is one +* remove variables with high unique rate +* converts character variables to factor +* remove variables with missing value + +#### Cleanse dataset with `cleanse()` +For example, we can cleanse all variables in `data_exam`: + +```{r cleanse,} +library(alookr) + +# cleansing dataset +newDat <- cleanse(data_exam) + +# structure of cleansing dataset +str(newDat) +``` + +* `remove variables whose unique value is one` : The year variable has only one value, "2018". Not needed when fitting the model. So it was removed. +* `remove variables with high unique rate` : If the number of levels of categorical data is very large, it is not suitable for classification model. In this case, it is highly likely to be an identifier of the data. So, remove the categorical (or character) variable with a high value of the unique rate defined as "number of levels / number of observations". + + The unique rate of the id variable with the number of levels of 1000 is 1. This variable is the object of the removal by identifier. + + The unique rate of the alpha variable is 0.026 and this variable is also removed. +* `converts character variables to factor` : The character type flag variable is converted to a factor type. + +For example, we can not remove the categorical data that is removed by changing the threshold of the `unique rate`: + +```{r cleanse_2} +# cleansing dataset +newDat <- cleanse(data_exam, uniq_thres = 0.03) + +# structure of cleansing dataset +str(newDat) +``` + +The `alpha` variable was not removed. + +If you do not want to apply a unique rate, you can set the value of the `uniq` argument to FALSE.: + +```{r cleanse_3} +# cleansing dataset +newDat <- cleanse(data_exam, uniq = FALSE) + +# structure of cleansing dataset +str(newDat) +``` + +If you do not want to force type conversion of a character variable to factor, you can set the value of the `char` argument to FALSE.: + +```{r cleanse_4} +# cleansing dataset +newDat <- cleanse(data_exam, char = FALSE) + +# structure of cleansing dataset +str(newDat) +``` + +If you want to remove a variable that contains missing values, specify the value of the `missing` argument as TRUE. The following example **removes the flag variable** that contains the missing value. + +```{r cleanse_5} +data_exam$flag[1] <- NA + +# cleansing dataset +newDat <- cleanse(data_exam, missing = TRUE) + +# structure of cleansing dataset +str(newDat) +``` + +### Diagnosis and removal of highly correlated variables + +In the linear model, there is a multicollinearity if there is a strong correlation between independent variables. So it is better to remove one variable from a pair of variables where the correlation exists. + +Even if it is not a linear model, removing one variable from a strongly correlated pair of variables can also reduce the overhead of the operation. It is also easy to interpret the model. + +#### Cleanse dataset with `treatment_corr()` +`treatment_corr()` diagnose pairs of highly correlated variables or remove on of them. + +`treatment_corr()` calculates correlation coefficient of pearson for numerical variable, and correlation coefficient of spearman for categorical variable. + +For example, we can diagnosis and removal of highly correlated variables: + +```{r treatment_corr} +# numerical variable +x1 <- 1:100 +set.seed(12L) +x2 <- sample(1:3, size = 100, replace = TRUE) * x1 + rnorm(1) +set.seed(1234L) +x3 <- sample(1:2, size = 100, replace = TRUE) * x1 + rnorm(1) + +# categorical variable +x4 <- factor(rep(letters[1:20], time = 5)) +set.seed(100L) +x5 <- factor(rep(letters[1:20 + sample(1:6, size = 20, replace = TRUE)], time = 5)) +set.seed(200L) +x6 <- factor(rep(letters[1:20 + sample(1:3, size = 20, replace = TRUE)], time = 5)) +set.seed(300L) +x7 <- factor(sample(letters[1:5], size = 100, replace = TRUE)) + +exam <- data.frame(x1, x2, x3, x4, x5, x6, x7) +str(exam) +head(exam) + +# default case +exam_01 <- treatment_corr(exam) +head(exam_01) + +# not removing variables +treatment_corr(exam, treat = FALSE) + +# Set a threshold to detecting variables when correlation greater then 0.9 +treatment_corr(exam, corr_thres = 0.9, treat = FALSE) + +# not verbose mode +exam_02 <- treatment_corr(exam, verbose = FALSE) +head(exam_02) +``` + +* `remove variables whose strong correlation` : x1, x4, x5 are removed. + +## Split the data into a train set and a test set + +### Data: Credit Card Default Data + +`Default` of `ISLR package` is a simulated data set containing information on ten thousand customers. The aim here is to predict which customers will default on their credit card debt. + +A data frame with 10000 observations on the following 4 variables.: + +* `default` : factor. A factor with levels No and Yes indicating whether the customer defaulted on their debt +* `student`: factor. A factor with levels No and Yes indicating whether the customer is a student +* `balance`: numeric. The average balance that the customer has remaining on their credit card after making their monthly payment +* `income` : numeric. Income of customer + +```{r create_data_2} +# Credit Card Default Data +head(ISLR::Default) + +# structure of dataset +str(ISLR::Default) + +# summary of dataset +summary(ISLR::Default) +``` + +### Split dataset +`split_by()` splits the data.frame or tbl_df into a training set and a test set. + +#### Split dataset with `split_by()` +The `split_df` class is created, which contains the split information and criteria to separate the training and the test set. + +```{r splits, message=FALSE} +library(alookr) +library(dplyr) + +# Generate data for the example +sb <- ISLR::Default %>% + split_by(default, seed = 6534) + +sb +``` + +The attributes of the `split_df` class are as follows.: + +* split_seed : integer. random seed used for splitting +* target : character. the name of the target variable +* binary : logical. whether the target variable is binary class +* minority : character. the name of the minority class +* majority : character. the name of the majority class +* minority_rate : numeric. the rate of the minority class +* majority_rate : numeric. the rate of the majority class + +```{r attr} +attr_names <- names(attributes(sb)) +attr_names + +sb_attr <- attributes(sb) + +# The third property, row.names, is excluded from the output because its length is very long. +sb_attr[!attr_names %in% "row.names"] +``` + +`summary()` summarizes the information of two datasets splitted by `split_by()`. + +```{r summ} +summary(sb) +``` + + +### Compare dataset +Train data and test data should be similar. If the two datasets are not similar, the performance of the predictive model may be reduced. + +`alookr` provides a function to compare the similarity between train dataset and test dataset. + +If the two data sets are not similar, the train dataset and test dataset should be splitted again from the original data. + +#### Comparison of categorical variables with `compare_target_category()` + +Compare the statistics of the categorical variables of the train set and test set included in the "split_df" class. + +```{r compare_target_category} +sb %>% + compare_target_category() + +# compare variables that are character data types. +sb %>% + compare_target_category(add_character = TRUE) + +# display marginal +sb %>% + compare_target_category(margin = TRUE) + +# student variable only +sb %>% + compare_target_category(student) + +sb %>% + compare_target_category(student, margin = TRUE) +``` + +compare_target_category() returns tbl_df, where the variables have the following.: + +* variable : character. categorical variable name +* level : factor. level of categorical variables +* train : numeric. the relative frequency of the level in the train set +* test : numeric. the relative frequency of the level in the test set +* abs_diff : numeric. the absolute value of the difference between two relative frequencies + +#### Comparison of numeric variables with `compare_target_numeric()` + +Compare the statistics of the numerical variables of the train set and test set included in the "split_df" class. + +```{r compare_target_numeric} +sb %>% + compare_target_numeric() + +# balance variable only +sb %>% + compare_target_numeric(balance) +``` + +compare_target_numeric() returns tbl_df, where the variables have the following.: + +* variable : character. numeric variable name +* train_mean : numeric. arithmetic mean of train set +* test_mean : numeric. arithmetic mean of test set +* train_sd : numeric. standard deviation of train set +* test_sd : numeric. standard deviation of test set +* train_z : numeric. the arithmetic mean of the train set divided by the standard deviation +* test_z : numeric. the arithmetic mean of the test set divided by the standard deviation + +#### Comparison plot with `compare_plot()` + +Plot compare information of the train set and test set included in the "split_df" class. + +```{r compare_plot, fig.height=5, fig.width=6, message=FALSE} +# income variable only +sb %>% + compare_plot("income") + +# all varibales +sb %>% + compare_plot() +``` + +#### Diagnosis of train set and test set with `compare_diag()` + +Diagnosis of similarity between datasets splitted by train set and set included in the "split_df" class. + +```{r create_dataset} +defaults <- ISLR::Default +defaults$id <- seq(NROW(defaults)) + +set.seed(1) +defaults[sample(seq(NROW(defaults)), 3), "student"] <- NA +set.seed(2) +defaults[sample(seq(NROW(defaults)), 10), "balance"] <- NA + +sb_2 <- defaults %>% + split_by(default) + +sb_2 %>% + compare_diag() + +sb_2 %>% + compare_diag(add_character = TRUE) + +sb_2 %>% + compare_diag(uniq_thres = 0.0005) +``` + + +### Extract train/test dataset +If you compare the train set with the test set and find that the two datasets are similar, extract the data from the split_df object. + +#### Extract train set or test set with `extract_set()` + +Extract train set or test set from split_df class object. + +```{r extract_set} +train <- sb %>% + extract_set(set = "train") + +test <- sb %>% + extract_set(set = "test") + +dim(train) + +dim(test) +``` + +#### Extract the data to fit the model with `sampling_target()` +In a target class, the ratio of the majority class to the minority class is not similar and the ratio of the minority class is very small, which is called the `imbalanced class`. + +If target variable is an imbalanced class, the characteristics of the majority class are actively reflected in the model. This model implies an error in predicting the minority class as the majority class. So we have to make the train dataset a balanced class. + +`sampling_target()` performs sampling on the train set of split_df to resolve the imbalanced class. + +```{r sampling_target} +# under-sampling with random seed +under <- sb %>% + sampling_target(seed = 1234L) + +under %>% + count(default) + +# under-sampling with random seed, and minority class frequency is 40% +under40 <- sb %>% + sampling_target(seed = 1234L, perc = 40) + +under40 %>% + count(default) + +# over-sampling with random seed +over <- sb %>% + sampling_target(method = "ubOver", seed = 1234L) + +over %>% + count(default) + +# over-sampling with random seed, and k = 10 +over10 <- sb %>% + sampling_target(method = "ubOver", seed = 1234L, k = 10) + +over10 %>% + count(default) + +# SMOTE with random seed +smote <- sb %>% + sampling_target(method = "ubSMOTE", seed = 1234L) + +smote %>% + count(default) + +# SMOTE with random seed, and perc.under = 250 +smote250 <- sb %>% + sampling_target(method = "ubSMOTE", seed = 1234L, perc.under = 250) + +smote250 %>% + count(default) +``` + +The argument that specifies the sampling method in sampling_target () is method. "ubUnder" is under-sampling, and "ubOver" is over-sampling, "ubSMOTE" is SMOTE(Synthetic Minority Over-sampling TEchnique). + +## Modeling and Evaluate, Predict + +### Data: Wisconsin Breast Cancer Data +`BreastCancer` of `mlbench package` is a breast cancer data. The objective is to identify each of a number of benign or malignant classes. + +A data frame with 699 observations on 11 variables, one being a character variable, 9 being ordered or nominal, and 1 target class.: + +* `Id` : character. Sample code number +* `Cl.thickness` : ordered factor. Clump Thickness +* `Cell.size` : ordered factor. Uniformity of Cell Size +* `Cell.shape` : ordered factor. Uniformity of Cell Shape +* `Marg.adhesion` : ordered factor. Marginal Adhesion +* `Epith.c.size` : ordered factor. Single Epithelial Cell Size +* `Bare.nuclei` : factor. Bare Nuclei +* `Bl.cromatin` : factor. Bland Chromatin +* `Normal.nucleoli` : factor. Normal Nucleoli +* `Mitoses` : factor. Mitoses +* `Class` : factor. Class. level is `benign` and `malignant`. + +```{r load_data} +library(mlbench) +data(BreastCancer) + +# class of each variables +sapply(BreastCancer, function(x) class(x)[1]) +``` + +### Preperation the data +Perform data preprocessing as follows.: + +* Find and imputate variables that contain missing values. +* Split the data into a train set and a test set. +* To solve the imbalanced class, perform sampling in the train set of raw data. +* Cleansing the dataset for classification modeling. + +#### Fix the missing value with `dlookr::imputate_na()` +find the variables that include missing value. and imputate the missing value using imputate_na() in dlookr package. + +```{r imputate_data, message=FALSE, warning=FALSE} +library(dlookr) +library(dplyr) + +# variable that have a missing value +diagnose(BreastCancer) %>% + filter(missing_count > 0) + +# imputation of missing value +breastCancer <- BreastCancer %>% + mutate(Bare.nuclei = imputate_na(BreastCancer, Bare.nuclei, Class, + method = "mice", no_attrs = TRUE, print_flag = FALSE)) +``` + +### Split data set +#### Splits the dataset into a train set and a test set with `split_by()` + +`split_by()` in the alookr package splits the dataset into a train set and a test set. + +The ratio argument of the `split_by()` function specifies the ratio of the train set. + +`split_by()` creates a class object named split_df. + +```{r split_data, warning=FALSE} +library(alookr) + +# split the data into a train set and a test set by default arguments +sb <- breastCancer %>% + split_by(target = Class) + +# show the class name +class(sb) + +# split the data into a train set and a test set by ratio = 0.6 +tmp <- breastCancer %>% + split_by(Class, ratio = 0.6) +``` + +The `summary()` function displays the following useful information about the split_df object: + +* random seed : The random seed is the random seed used internally to separate the data +* split data : Information of splited data + + train set count : number of train set + + test set count : number of test set +* target variable : Target variable name + + minority class : name and ratio(In parentheses) of minority class + + majority class : name and ratio(In parentheses) of majority class + +```{r split_summary, warning=FALSE} +# summary() display the some information +summary(sb) + +# summary() display the some information +summary(tmp) +``` + +#### Check missing levels in the train set + +In the case of categorical variables, when a train set and a test set are separated, a specific level may be missing from the train set. + +In this case, there is no problem when fitting the model, but an error occurs when predicting with the model you created. Therefore, preprocessing is performed to avoid missing data preprocessing. + +In the following example, fortunately, there is no categorical variable that contains the missing levels in the train set. + +```{r split_check, warning=FALSE} +# list of categorical variables in the train set that contain missing levels +nolevel_in_train <- sb %>% + compare_target_category() %>% + filter(is.na(train)) %>% + select(variable) %>% + unique() %>% + pull + +nolevel_in_train + +# if any of the categorical variables in the train set contain a missing level, +# split them again. +while (length(nolevel_in_train) > 0) { + sb <- breastCancer %>% + split_by(Class) + + nolevel_in_train <- sb %>% + compare_target_category() %>% + filter(is.na(train)) %>% + select(variable) %>% + unique() %>% + pull +} +``` + +### Handling the imbalanced classes data with `sampling_target()` + +#### Issue of imbalanced classes data +Imbalanced classes(levels) data means that the number of one level of the frequency of the target variable is relatively small. +In general, the proportion of positive classes is relatively small. For example, in the model of predicting spam, the class of interest spam is less than non-spam. + +Imbalanced classes data is a common problem in machine learning classification. + +`table()` and `prop.table()` are traditionally useful functions for diagnosing imbalanced classes data. However, alookr's `summary()` is simpler and provides more information. + +```{r show_ratio, warning=FALSE} +# train set frequency table - imbalanced classes data +table(sb$Class) + +# train set relative frequency table - imbalanced classes data +prop.table(table(sb$Class)) + +# using summary function - imbalanced classes data +summary(sb) +``` + +#### Handling the imbalanced classes data + +Most machine learning algorithms work best when the number of samples in each class are about equal. And most algorithms are designed to maximize accuracy and reduce error. So, we requre handling an imbalanced class problem. + +sampling_target() performs sampling to solve an imbalanced classes data problem. + +#### Resampling - oversample minority class +Oversampling can be defined as adding more copies of the minority class. + +Oversampling is performed by specifying "ubOver" in the method argument of the `sampling_target()` function. + +```{r sampling_over, warning=FALSE} +# to balanced by over sampling +train_over <- sb %>% + sampling_target(method = "ubOver") + +# frequency table +table(train_over$Class) +``` + +#### Resampling - undersample majority class +Undersampling can be defined as removing some observations of the majority class. + +Undersampling is performed by specifying "ubUnder" in the method argument of the `sampling_target()` function. + +```{r sampling_under, warning=FALSE} +# to balanced by under sampling +train_under <- sb %>% + sampling_target(method = "ubUnder") + +# frequency table +table(train_under$Class) +``` + +#### Generate synthetic samples - SMOTE +SMOTE(Synthetic Minority Oversampling Technique) uses a nearest neighbors algorithm to generate new and synthetic data. + +SMOTE is performed by specifying "ubSMOTE" in the method argument of the `sampling_target()` function. + +```{r sampling_smote, warning=FALSE} +# to balanced by SMOTE +train_smote <- sb %>% + sampling_target(seed = 1234L, method = "ubSMOTE") + +# frequency table +table(train_smote$Class) +``` + +### Cleansing the dataset for classification modeling with `cleanse()` +The `cleanse()` cleanse the dataset for classification modeling. + +This function is useful when fit the classification model. This function does the following.: + +* Remove the variable with only one value. +* And remove variables that have a unique number of values relative to the number of observations for a character or categorical variable. + + In this case, it is a variable that corresponds to an identifier or an identifier. +* And converts the character to factor. + +In this example, The `cleanse()` function removed a variable ID with a high unique rate. + +```{r clean_data, warning=FALSE} +# clean the training set +train <- train_smote %>% + cleanse +``` + +### Extract test set for evaluation of the model with `extract_set()` + +```{r extract_test, warning=FALSE} +# extract test set +test <- sb %>% + extract_set(set = "test") +``` + +### Binary classification modeling with `run_models()` +`run_models()` performs some representative binary classification modeling using `split_df` object created by `split_by()`. + +`run_models()` executes the process in parallel when fitting the model. However, it is not supported in MS-Windows operating system and RStudio environment. + +Currently supported algorithms are as follows.: + +* logistic : logistic regression using `stats` package +* rpart : Recursive Partitioning Trees using `rpart` package +* ctree : Conditional Inference Trees using `party` package +* randomForest :Classification with Random Forest using `randomForest` package +* ranger : A Fast Implementation of Random Forests using `ranger` package + +`run_models()` returns a `model_df` class object. + +The `model_df` class object contains the following variables.: + +* step : character. The current stage in the classification modeling process. + + For objects created with `run_models()`, the value of the variable is "1.Fitted". +* model_id : model identifiers +* target : name of target variable +* positive : positive class in target variable +* fitted_model : list. Fitted model object by model_id's algorithms + + +```{r fit_model, message=FALSE, warning=FALSE} +result <- train %>% + run_models(target = "Class", positive = "malignant") +result +``` + +### Evaluate the model +Evaluate the predictive performance of fitted models. + +#### Predict test set using fitted model with `run_predict()` + +`run_predict()` predict the test set using `model_df` class fitted by `run_models()`. + +`run_predict ()` is executed in parallel when predicting by model. However, it is not supported in MS-Windows operating system and RStudio environment. + +The `model_df` class object contains the following variables.: + +* step : character. The current stage in the classification modeling process. + + For objects created with `run_predict()`, the value of the variable is "2.Predicted". +* model_id : character. Type of fit model. +* target : character. Name of target variable. +* positive : character. Level of positive class of binary classification. +* fitted_model : list. Fitted model object by model_id's algorithms. +* predicted : result of predcit by each models + +```{r predict} +pred <- result %>% + run_predict(test) +pred +``` + +#### Calculate the performance metric with `run_performance()` + +`run_performance()` calculate the performance metric of `model_df` class predicted by `run_predict()`. + +`run_performance ()` is performed in parallel when calculating the performance evaluation index. However, it is not supported in MS-Windows operating system and RStudio environment. + +The `model_df` class object contains the following variables.: + +* step : character. The current stage in the classification modeling process. + + For objects created with `run_performance()`, the value of the variable is "3.Performanced". +* model_id : character. Type of fit model. +* target : character. Name of target variable. +* positive : character. Level of positive class of binary classification. +* fitted_model : list. Fitted model object by model_id's algorithms +* predicted : list. Predicted value by individual model. Each value has a predict_class class object. +* performance : list. Calculate metrics by individual model. Each value has a numeric vector. + +```{r performance1} +# Calculate performace metrics. +perf <- run_performance(pred) +perf +``` + +The performance variable contains a list object, which contains 15 performance metrics: + +* ZeroOneLoss : Normalized Zero-One Loss(Classification Error Loss). +* Accuracy : Accuracy. +* Precision : Precision. +* Recall : Recall. +* Sensitivity : Sensitivity. +* Specificity : Specificity. +* F1_Score : F1 Score. +* Fbeta_Score : F-Beta Score. +* LogLoss : Log loss / Cross-Entropy Loss. +* AUC : Area Under the Receiver Operating Characteristic Curve (ROC AUC). +* Gini : Gini Coefficient. +* PRAUC : Area Under the Precision-Recall Curve (PR AUC). +* LiftAUC : Area Under the Lift Chart. +* GainAUC : Area Under the Gain Chart. +* KS_Stat : Kolmogorov-Smirnov Statistic. + +```{r performance2} +# Performance by analytics models +performance <- perf$performance +names(performance) <- perf$model_id +performance +``` + +If you change the list object to tidy format, you'll see the following at a glance: + +```{r performance3} +# Convert to matrix for compare performace. +sapply(performance, "c") +``` + +`compare_performance()` return a list object(results of compared model performance). and list has the following components: + +* recommend_model : character. The name of the model that is recommended as the best among the various models. +* top_count : numeric. The number of best performing performance metrics by model. +* mean_rank : numeric. Average of ranking individual performance metrics by model. +* top_metric : list. The name of the performance metric with the best performance on individual performance metrics by model. + +In this example, `compare_performance()` recommend the **"ranger"** model. + +```{r compare_performance} +# Compaire the Performance metrics of each model +comp_perf <- compare_performance(pred) +comp_perf +``` + +#### Plot the ROC curve with `plot_performance()` + +`compare_performance()` plot ROC curve. + +```{r ROC, fig.height=4, fig.width=7} +# Plot ROC curve +plot_performance(pred) +``` + + +#### Tunning the cut-off + +In general, if the prediction probability is greater than 0.5 in the binary classification model, it is predicted as `positive class`. +In other words, 0.5 is used for the cut-off value. +This applies to most model algorithms. However, in some cases, the performance can be tuned by changing the cut-off value. + +`plot_cutoff ()` visualizes a plot to select the cut-off value, and returns the cut-off value. + +```{r cutoff, warning=FALSE, fig.height=4, fig.width=7} +pred_best <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + select(predicted) %>% + pull %>% + .[[1]] %>% + attr("pred_prob") + +cutoff <- plot_cutoff(pred_best, test$Class, "malignant", type = "mcc") +cutoff + +cutoff2 <- plot_cutoff(pred_best, test$Class, "malignant", type = "density") +cutoff2 + +cutoff3 <- plot_cutoff(pred_best, test$Class, "malignant", type = "prob") +cutoff3 +``` + +#### Performance comparison between prediction and tuned cut-off with `performance_metric()` + +Compare the performance of the original prediction with that of the tuned cut-off. +Compare the cut-off with the non-cut model for the model with the best performance `comp_perf$recommend_model`. + +```{r predit_cutoff} +comp_perf$recommend_model + +# extract predicted probability +idx <- which(pred$model_id == comp_perf$recommend_model) +pred_prob <- attr(pred$predicted[[idx]], "pred_prob") + +# or, extract predicted probability using dplyr +pred_prob <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + select(predicted) %>% + pull %>% + "[["(1) %>% + attr("pred_prob") + +# predicted probability +pred_prob + +# compaire Accuracy +performance_metric(pred_prob, test$Class, "malignant", "Accuracy") +performance_metric(pred_prob, test$Class, "malignant", "Accuracy", + cutoff = cutoff) + +# compaire Confusion Matrix +performance_metric(pred_prob, test$Class, "malignant", "ConfusionMatrix") +performance_metric(pred_prob, test$Class, "malignant", "ConfusionMatrix", + cutoff = cutoff) + +# compaire F1 Score +performance_metric(pred_prob, test$Class, "malignant", "F1_Score") +performance_metric(pred_prob, test$Class, "malignant", "F1_Score", + cutoff = cutoff) +performance_metric(pred_prob, test$Class, "malignant", "F1_Score", + cutoff = cutoff2) +``` + +If the performance of the tuned cut-off is good, use it as a cut-off to predict positives. + +### Predict +If you have selected a good model from several models, then perform the prediction with that model. + +#### Create data set for predict +Create sample data for predicting by extracting 100 samples from the data set used in the previous under sampling example. + +```{r predit_data, warning=FALSE} +data_pred <- train_under %>% + cleanse + +set.seed(1234L) +data_pred <- data_pred %>% + nrow %>% + seq %>% + sample(size = 50) %>% + data_pred[., ] +``` + +#### Predict with alookr and dplyr +Do a predict using the `dplyr` package. The last `factor()` function eliminates unnecessary information. + +```{r predit_final, warning=FALSE} +pred_actual <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + run_predict(data_pred) %>% + select(predicted) %>% + pull %>% + "[["(1) %>% + factor() + +pred_actual +``` + +If you want to predict by cut-off, specify the `cutoff` argument in the `run_predict()` function as follows.: + +In the example, there is no difference between the results of using cut-off and not. + +```{r predit_final2, warning=FALSE} +pred_actual2 <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + run_predict(data_pred, cutoff) %>% + select(predicted) %>% + pull %>% + "[["(1) %>% + factor() + +pred_actual2 + +sum(pred_actual != pred_actual2) +``` + + diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..21967d9 --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,52 @@ +pandoc: '2.8' +pkgdown: 1.5.1 +pkgdown_sha: ~ + +title: alookr + +template: + params: + bootswatch: united + +reference: +- title: Model Classifier for Binary Classification + desc: A collection of tools that support data cleansing and splitting, predictive modeling, and model evaluation. +# contents: +# - alookr-package +- subtitle: Cleansing the dataset + contents: + - starts_with("cleanse") + - cleanse.split_df + - treatment_corr +- subtitle: Splitting the dataset + contents: + - split_by + - summary.split_df + - starts_with("compare_") + - extract_set + - sampling_target +- subtitle: Classification Modeling + contents: + - starts_with("run_") + - plot_performance + - plot_cutoff + - performance_metric + - matthews + +navbar: + title: dlookr + type: default + left: + - text: Cleansing the dataset + href: articles/cleansing.html + - text: Splitting the dataset + href: articles/split.html + - text: Classification Modeling + href: articles/modeling.html + right: + - text: Function reference + href: reference/index.html + - icon: fa-github fa-lg + href: https://github.com/choonghyunryu/alookr/ +last_built: 2020-08-02T11:20Z + diff --git a/alookr.Rproj b/alookr.Rproj new file mode 100644 index 0000000..9dbf13d --- /dev/null +++ b/alookr.Rproj @@ -0,0 +1,18 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: knitr +LaTeX: pdfLaTeX + +BuildType: Package +PackageUseDevtools: Yes +PackageInstallArgs: --no-multiarch --with-keep.source +PackageCheckArgs: --as-cran diff --git a/docs/404.html b/docs/404.html index 462e45c..69fc4d9 100644 --- a/docs/404.html +++ b/docs/404.html @@ -6,12 +6,19 @@ Page not found (404) • alookr + + + + + + + + + + + + @@ -13,6 +19,7 @@ + Articles • alookrArticles • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -74,8 +74,7 @@

All vignettes

-

Site built with pkgdown -2.0.2.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/articles/introduce.html b/docs/articles/introduce.html index 66e48b4..cae669c 100644 --- a/docs/articles/introduce.html +++ b/docs/articles/introduce.html @@ -6,6 +6,12 @@ Introduce alookr • alookr + + + + + + @@ -13,6 +19,7 @@ + + + + + + @@ -13,6 +19,7 @@ + + + + + + @@ -13,6 +19,7 @@ + Authors and Citation • alookrAuthors and Citation • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -67,15 +67,16 @@

Citation

-

Ryu C (2022). +

Ryu C (2024). alookr: Model Classifier for Binary Classification. -R package version 0.3.7. +R package version 0.3.9, https://choonghyunryu.github.io/alookr/. 

@Manual{,
   title = {alookr: Model Classifier for Binary Classification},
   author = {Choonghyun Ryu},
-  year = {2022},
-  note = {R package version 0.3.7},
+  year = {2024},
+  note = {R package version 0.3.9},
+  url = {https://choonghyunryu.github.io/alookr/},
 }
@@ -89,8 +90,7 @@

Citation

-

Site built with pkgdown -2.0.2.

+

Site built with pkgdown 2.0.7.

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Another feature is to support the development of predictive models and to compare the performance of several predictive models, helping to select the best model. "> +

Overview @@ -89,34 +96,28 @@

OverviewClean and split data sets to train and test.
  • Create several representative models.
  • Evaluate the performance of the model to select the best model.
    • -
  • Support the entire process of developing a binary classification -model.
    • +
  • Support the entire process of developing a binary classification model.
    • -

    The name alookr comes from -looking at the analytics process in the data analysis -process.

    +

    The name alookr comes from looking at the analytics process in the data analysis process.

    Install alookr

    The released version is available on CRAN. but not yet.

    -install.packages("alookr")
    -

    Or you can get the development version without vignettes from -GitHub:

    +install.packages("alookr")
    +

    Or you can get the development version without vignettes from GitHub:

    -devtools::install_github("choonghyunryu/alookr")
    -

    Or you can get the development version with vignettes from -GitHub:

    +devtools::install_github("choonghyunryu/alookr") +

    Or you can get the development version with vignettes from GitHub:

    -install.packages(c("ISLR", "spelling", "mlbench"))
-devtools::install_github("choonghyunryu/alookr", build_vignettes = TRUE)
    +install.packages(c("ISLR", "spelling", "mlbench")) +devtools::install_github("choonghyunryu/alookr", build_vignettes = TRUE)

    Usage

    -

    alookr includes several vignette files, which we use throughout the -documentation.

    +

    alookr includes several vignette files, which we use throughout the documentation.

    Provided vignettes is as follows.

    • Cleansing the dataset
      • @@ -124,7 +125,7 @@

      Usage
    • Modeling and Evaluate, Predict

    -browseVignettes(package = "alookr")
    +browseVignettes(package = "alookr")

    Cleansing the dataset @@ -132,9 +133,7 @@

    Cleansing the dataset

    Data: create example dataset

    -

    To illustrate basic use of the alookr package, create the -data_exam with sample function. The data_exam -dataset include 5 variables.

    +

    To illustrate basic use of the alookr package, create the data_exam with sample function. The data_exam dataset include 5 variables.

    variables are as follows.:

    • @@ -149,55 +148,54 @@

      Data: create example dataset
      -# create sample dataset
-set.seed(123L)
-id <- sapply(1:1000, function(x)
-  paste(c(sample(letters, 5), x), collapse = ""))
-
-year <- "2018"
-
-set.seed(123L)
-count <- sample(1:10, size = 1000, replace = TRUE)
-
-set.seed(123L)
-alpha <- sample(letters, size = 1000, replace = TRUE)
-
-set.seed(123L)
-flag <- sample(c("Y", "N"), size = 1000, prob = c(0.1, 0.9), replace = TRUE)
-
-data_exam <- data.frame(id, year, count, alpha, flag, stringsAsFactors = FALSE)
-
-# structure of dataset
-str(data_exam)
-#> 'data.frame':    1000 obs. of  5 variables:
-#>  $ id   : chr  "osncj1" "rvket2" "nvesi3" "chgji4" ...
-#>  $ year : chr  "2018" "2018" "2018" "2018" ...
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: chr  "o" "s" "n" "c" ...
-#>  $ flag : chr  "N" "N" "N" "N" ...
-
-# summary of dataset
-summary(data_exam)
-#>       id                year               count           alpha          
-#>  Length:1000        Length:1000        Min.   : 1.000   Length:1000       
-#>  Class :character   Class :character   1st Qu.: 3.000   Class :character  
-#>  Mode  :character   Mode  :character   Median : 6.000   Mode  :character  
-#>                                        Mean   : 5.698                     
-#>                                        3rd Qu.: 8.000                     
-#>                                        Max.   :10.000                     
-#>      flag          
-#>  Length:1000       
-#>  Class :character  
-#>  Mode  :character  
-#>                    
-#>                    
-#>

    +# create sample dataset +set.seed(123L) +id <- sapply(1:1000, function(x) + paste(c(sample(letters, 5), x), collapse = "")) + +year <- "2018" + +set.seed(123L) +count <- sample(1:10, size = 1000, replace = TRUE) + +set.seed(123L) +alpha <- sample(letters, size = 1000, replace = TRUE) + +set.seed(123L) +flag <- sample(c("Y", "N"), size = 1000, prob = c(0.1, 0.9), replace = TRUE) + +data_exam <- data.frame(id, year, count, alpha, flag, stringsAsFactors = FALSE) + +# structure of dataset +str(data_exam) +#> 'data.frame': 1000 obs. of 5 variables: +#> $ id : chr "osncj1" "rvket2" "nvesi3" "chgji4" ... +#> $ year : chr "2018" "2018" "2018" "2018" ... +#> $ count: int 3 3 10 2 6 5 4 6 9 10 ... +#> $ alpha: chr "o" "s" "n" "c" ... +#> $ flag : chr "N" "N" "N" "N" ... + +# summary of dataset +summary(data_exam) +#> id year count alpha +#> Length:1000 Length:1000 Min. : 1.000 Length:1000 +#> Class :character Class :character 1st Qu.: 3.000 Class :character +#> Mode :character Mode :character Median : 6.000 Mode :character +#> Mean : 5.698 +#> 3rd Qu.: 8.000 +#> Max. :10.000 +#> flag +#> Length:1000 +#> Class :character +#> Mode :character +#> +#> +#>

    Clean dataset

    -

    cleanse() cleans up the dataset before fitting the -classification model.

    +

    cleanse() cleans up the dataset before fitting the classification model.

    The function of cleanse() is as follows.:

    • remove variables whose unique value is one
      • @@ -209,265 +207,237 @@

      Clean datasetCleanse dataset with cleanse()

      -

      For example, we can cleanse all variables in -data_exam:

      +

      For example, we can cleanse all variables in data_exam:

      -library(alookr)
-#> Loading required package: ggplot2
-#> Loading required package: randomForest
-#> randomForest 4.6-14
-#> Type rfNews() to see new features/changes/bug fixes.
-#> 
-#> Attaching package: 'randomForest'
-#> The following object is masked from 'package:ggplot2':
-#> 
-#>     margin
-
-# cleansing dataset
-newDat <- cleanse(data_exam)
-#> ── Checking unique value ─────────────────────────── unique value is one ──
-#> remove variables that unique value is one
-#> ● year
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
-#> remove variables with high unique rate
-#> ● id = 1000(1)
-#> 
-#> ── Checking character variables ─────────────────────── categorical data ──
-#> converts character variables to factor
-#> ● alpha
-#> ● flag
-
-# structure of cleansing dataset
-str(newDat)
-#> 'data.frame':    1000 obs. of  3 variables:
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
-#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
      +library(alookr) +#> Loading required package: ggplot2 +#> Loading required package: randomForest +#> randomForest 4.6-14 +#> Type rfNews() to see new features/changes/bug fixes. +#> +#> Attaching package: 'randomForest' +#> The following object is masked from 'package:ggplot2': +#> +#> margin + +# cleansing dataset +newDat <- cleanse(data_exam) +#> ── Checking unique value ─────────────────────────── unique value is one ── +#> remove variables that unique value is one +#> ● year +#> +#> ── Checking unique rate ─────────────────────────────── high unique rate ── +#> remove variables with high unique rate +#> ● id = 1000(1) +#> +#> ── Checking character variables ─────────────────────── categorical data ── +#> converts character variables to factor +#> ● alpha +#> ● flag + +# structure of cleansing dataset +str(newDat) +#> 'data.frame': 1000 obs. of 3 variables: +#> $ count: int 3 3 10 2 6 5 4 6 9 10 ... +#> $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ... +#> $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
    • -remove variables whose unique value is one : The year -variable has only one value, “2018”. Not needed when fitting the model. -So it was removed.
      • +remove variables whose unique value is one : The year variable has only one value, “2018”. Not needed when fitting the model. So it was removed.
    • -remove variables with high unique rate : If the number -of levels of categorical data is very large, it is not suitable for -classification model. In this case, it is highly likely to be an -identifier of the data. So, remove the categorical (or character) -variable with a high value of the unique rate defined as “number of -levels / number of observations”. +remove variables with high unique rate : If the number of levels of categorical data is very large, it is not suitable for classification model. In this case, it is highly likely to be an identifier of the data. So, remove the categorical (or character) variable with a high value of the unique rate defined as “number of levels / number of observations”.
        -
      • The unique rate of the id variable with the number of levels of 1000 -is 1. This variable is the object of the removal by identifier.
        • -
      • The unique rate of the alpha variable is 0.026 and this variable is -also removed.
        • +
      • The unique rate of the id variable with the number of levels of 1000 is 1. This variable is the object of the removal by identifier.
        • +
      • The unique rate of the alpha variable is 0.026 and this variable is also removed.
    • -converts character variables to factor : The character -type flag variable is converted to a factor type.
      • +converts character variables to factor : The character type flag variable is converted to a factor type.
    -

    For example, we can not remove the categorical data that is removed -by changing the threshold of the unique rate:

    +

    For example, we can not remove the categorical data that is removed by changing the threshold of the unique rate:

    -# cleansing dataset
-newDat <- cleanse(data_exam, uniq_thres = 0.03)
-#> ── Checking unique value ─────────────────────────── unique value is one ──
-#> remove variables that unique value is one
-#> ● year
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
-#> remove variables with high unique rate
-#> ● id = 1000(1)
-#> 
-#> ── Checking character variables ─────────────────────── categorical data ──
-#> converts character variables to factor
-#> ● alpha
-#> ● flag
-
-# structure of cleansing dataset
-str(newDat)
-#> 'data.frame':    1000 obs. of  3 variables:
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
-#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
    +# cleansing dataset +newDat <- cleanse(data_exam, uniq_thres = 0.03) +#> ── Checking unique value ─────────────────────────── unique value is one ── +#> remove variables that unique value is one +#> ● year +#> +#> ── Checking unique rate ─────────────────────────────── high unique rate ── +#> remove variables with high unique rate +#> ● id = 1000(1) +#> +#> ── Checking character variables ─────────────────────── categorical data ── +#> converts character variables to factor +#> ● alpha +#> ● flag + +# structure of cleansing dataset +str(newDat) +#> 'data.frame': 1000 obs. of 3 variables: +#> $ count: int 3 3 10 2 6 5 4 6 9 10 ... +#> $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ... +#> $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...

    The alpha variable was not removed.

    -

    If you do not want to apply a unique rate, you can set the value of -the uniq argument to FALSE.:

    +

    If you do not want to apply a unique rate, you can set the value of the uniq argument to FALSE.:

    -# cleansing dataset
-newDat <- cleanse(data_exam, uniq = FALSE)
-#> ── Checking character variables ─────────────────────── categorical data ──
-#> converts character variables to factor
-#> ● id
-#> ● year
-#> ● alpha
-#> ● flag
-
-# structure of cleansing dataset
-str(newDat)
-#> 'data.frame':    1000 obs. of  5 variables:
-#>  $ id   : Factor w/ 1000 levels "ablnc282","abqym54",..: 594 715 558 94 727 270 499 882 930 515 ...
-#>  $ year : Factor w/ 1 level "2018": 1 1 1 1 1 1 1 1 1 1 ...
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
-#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
    -

    If you do not want to force type conversion of a character variable -to factor, you can set the value of the char argument to -FALSE.:

    +# cleansing dataset +newDat <- cleanse(data_exam, uniq = FALSE) +#> ── Checking character variables ─────────────────────── categorical data ── +#> converts character variables to factor +#> ● id +#> ● year +#> ● alpha +#> ● flag + +# structure of cleansing dataset +str(newDat) +#> 'data.frame': 1000 obs. of 5 variables: +#> $ id : Factor w/ 1000 levels "ablnc282","abqym54",..: 594 715 558 94 727 270 499 882 930 515 ... +#> $ year : Factor w/ 1 level "2018": 1 1 1 1 1 1 1 1 1 1 ... +#> $ count: int 3 3 10 2 6 5 4 6 9 10 ... +#> $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ... +#> $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ... +

    If you do not want to force type conversion of a character variable to factor, you can set the value of the char argument to FALSE.:

    -# cleansing dataset
-newDat <- cleanse(data_exam, char = FALSE)
-#> ── Checking unique value ─────────────────────────── unique value is one ──
-#> remove variables that unique value is one
-#> ● year
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
-#> remove variables with high unique rate
-#> ● id = 1000(1)
-
-# structure of cleansing dataset
-str(newDat)
-#> 'data.frame':    1000 obs. of  3 variables:
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: chr  "o" "s" "n" "c" ...
-#>  $ flag : chr  "N" "N" "N" "N" ...
    -

    If you want to remove a variable that contains missing values, -specify the value of the missing argument as TRUE. The -following example removes the flag variable that -contains the missing value.

    +# cleansing dataset +newDat <- cleanse(data_exam, char = FALSE) +#> ── Checking unique value ─────────────────────────── unique value is one ── +#> remove variables that unique value is one +#> ● year +#> +#> ── Checking unique rate ─────────────────────────────── high unique rate ── +#> remove variables with high unique rate +#> ● id = 1000(1) + +# structure of cleansing dataset +str(newDat) +#> 'data.frame': 1000 obs. of 3 variables: +#> $ count: int 3 3 10 2 6 5 4 6 9 10 ... +#> $ alpha: chr "o" "s" "n" "c" ... +#> $ flag : chr "N" "N" "N" "N" ... +

    If you want to remove a variable that contains missing values, specify the value of the missing argument as TRUE. The following example removes the flag variable that contains the missing value.

    -data_exam$flag[1] <- NA 
-
-# cleansing dataset
-newDat <- cleanse(data_exam, missing = TRUE)
-#> ── Checking missing value ────────────────────────────────── included NA ──
-#> remove variables whose included NA
-#> ● flag
-#> 
-#> ── Checking unique value ─────────────────────────── unique value is one ──
-#> remove variables that unique value is one
-#> ● year
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
-#> remove variables with high unique rate
-#> ● id = 1000(1)
-#> 
-#> ── Checking character variables ─────────────────────── categorical data ──
-#> converts character variables to factor
-#> ● alpha
-
-# structure of cleansing dataset
-str(newDat)
-#> 'data.frame':    1000 obs. of  2 variables:
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
    +data_exam$flag[1] <- NA + +# cleansing dataset +newDat <- cleanse(data_exam, missing = TRUE) +#> ── Checking missing value ────────────────────────────────── included NA ── +#> remove variables whose included NA +#> ● flag +#> +#> ── Checking unique value ─────────────────────────── unique value is one ── +#> remove variables that unique value is one +#> ● year +#> +#> ── Checking unique rate ─────────────────────────────── high unique rate ── +#> remove variables with high unique rate +#> ● id = 1000(1) +#> +#> ── Checking character variables ─────────────────────── categorical data ── +#> converts character variables to factor +#> ● alpha + +# structure of cleansing dataset +str(newDat) +#> 'data.frame': 1000 obs. of 2 variables: +#> $ count: int 3 3 10 2 6 5 4 6 9 10 ... +#> $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...

    Diagnosis and removal of highly correlated variables

    -

    In the linear model, there is a multicollinearity if there is a -strong correlation between independent variables. So it is better to -remove one variable from a pair of variables where the correlation -exists.

    -

    Even if it is not a linear model, removing one variable from a -strongly correlated pair of variables can also reduce the overhead of -the operation. It is also easy to interpret the model.

    +

    In the linear model, there is a multicollinearity if there is a strong correlation between independent variables. So it is better to remove one variable from a pair of variables where the correlation exists.

    +

    Even if it is not a linear model, removing one variable from a strongly correlated pair of variables can also reduce the overhead of the operation. It is also easy to interpret the model.

    Cleanse dataset with treatment_corr()

    -

    treatment_corr() diagnose pairs of highly correlated -variables or remove on of them.

    -

    treatment_corr() calculates correlation coefficient of -pearson for numerical variable, and correlation coefficient of spearman -for categorical variable.

    -

    For example, we can diagnosis and removal of highly correlated -variables:

    +

    treatment_corr() diagnose pairs of highly correlated variables or remove on of them.

    +

    treatment_corr() calculates correlation coefficient of pearson for numerical variable, and correlation coefficient of spearman for categorical variable.

    +

    For example, we can diagnosis and removal of highly correlated variables:

    -# numerical variable
-x1 <- 1:100
-set.seed(12L)
-x2 <- sample(1:3, size = 100, replace = TRUE) * x1 + rnorm(1)
-set.seed(1234L)
-x3 <- sample(1:2, size = 100, replace = TRUE) * x1 + rnorm(1)
-
-# categorical variable
-x4 <- factor(rep(letters[1:20], time = 5))
-set.seed(100L)
-x5 <- factor(rep(letters[1:20 + sample(1:6, size = 20, replace = TRUE)], time = 5))
-set.seed(200L)
-x6 <- factor(rep(letters[1:20 + sample(1:3, size = 20, replace = TRUE)], time = 5))
-set.seed(300L)
-x7 <- factor(sample(letters[1:5], size = 100, replace = TRUE))
-
-exam <- data.frame(x1, x2, x3, x4, x5, x6, x7)
-str(exam)
-#> 'data.frame':    100 obs. of  7 variables:
-#>  $ x1: int  1 2 3 4 5 6 7 8 9 10 ...
-#>  $ x2: num  2.55 4.55 9.55 12.55 10.55 ...
-#>  $ x3: num  0.194 2.194 4.194 6.194 3.194 ...
-#>  $ x4: Factor w/ 20 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ...
-#>  $ x5: Factor w/ 13 levels "c","e","f","g",..: 1 5 3 2 4 7 6 8 9 8 ...
-#>  $ x6: Factor w/ 15 levels "c","d","f","g",..: 1 2 3 4 3 5 6 7 8 9 ...
-#>  $ x7: Factor w/ 5 levels "a","b","c","d",..: 2 2 1 4 5 1 4 3 1 5 ...
-head(exam)
-#>   x1        x2         x3 x4 x5 x6 x7
-#> 1  1  2.554297  0.1939687  a  c  c  b
-#> 2  2  4.554297  2.1939687  b  h  d  b
-#> 3  3  9.554297  4.1939687  c  f  f  a
-#> 4  4 12.554297  6.1939687  d  e  g  d
-#> 5  5 10.554297  3.1939687  e  g  f  e
-#> 6  6  6.554297 10.1939687  f  l  h  a
-
-# default case
-exam_01 <- treatment_corr(exam)
-#> * remove variables whose strong correlation (pearson >= 0.8)
-#>  - remove x1 : with x3 (0.825)
-#> * remove variables whose strong correlation (spearman >= 0.8)
-#>  - remove x4 : with x5 (0.9649)
-#>  - remove x4 : with x6 (0.9928)
-#>  - remove x5 : with x6 (0.9485)
-head(exam_01)
-#>          x2         x3 x6 x7
-#> 1  2.554297  0.1939687  c  b
-#> 2  4.554297  2.1939687  d  b
-#> 3  9.554297  4.1939687  f  a
-#> 4 12.554297  6.1939687  g  d
-#> 5 10.554297  3.1939687  f  e
-#> 6  6.554297 10.1939687  h  a
-
-# not removing variables
-treatment_corr(exam, treat = FALSE)
-#> * remove variables whose strong correlation (pearson >= 0.8)
-#>  - remove x1 : with x3 (0.825)
-#> * remove variables whose strong correlation (spearman >= 0.8)
-#>  - remove x4 : with x5 (0.9649)
-#>  - remove x4 : with x6 (0.9928)
-#>  - remove x5 : with x6 (0.9485)
-
-# Set a threshold to detecting variables when correlation greater then 0.9
-treatment_corr(exam, corr_thres = 0.9, treat = FALSE)
-#> * remove variables whose strong correlation (spearman >= 0.9)
-#>  - remove x4 : with x5 (0.9649)
-#>  - remove x4 : with x6 (0.9928)
-#>  - remove x5 : with x6 (0.9485)
-
-# not verbose mode
-exam_02 <- treatment_corr(exam, verbose = FALSE)
-head(exam_02)
-#>          x2         x3 x6 x7
-#> 1  2.554297  0.1939687  c  b
-#> 2  4.554297  2.1939687  d  b
-#> 3  9.554297  4.1939687  f  a
-#> 4 12.554297  6.1939687  g  d
-#> 5 10.554297  3.1939687  f  e
-#> 6  6.554297 10.1939687  h  a
    +# numerical variable +x1 <- 1:100 +set.seed(12L) +x2 <- sample(1:3, size = 100, replace = TRUE) * x1 + rnorm(1) +set.seed(1234L) +x3 <- sample(1:2, size = 100, replace = TRUE) * x1 + rnorm(1) + +# categorical variable +x4 <- factor(rep(letters[1:20], time = 5)) +set.seed(100L) +x5 <- factor(rep(letters[1:20 + sample(1:6, size = 20, replace = TRUE)], time = 5)) +set.seed(200L) +x6 <- factor(rep(letters[1:20 + sample(1:3, size = 20, replace = TRUE)], time = 5)) +set.seed(300L) +x7 <- factor(sample(letters[1:5], size = 100, replace = TRUE)) + +exam <- data.frame(x1, x2, x3, x4, x5, x6, x7) +str(exam) +#> 'data.frame': 100 obs. of 7 variables: +#> $ x1: int 1 2 3 4 5 6 7 8 9 10 ... +#> $ x2: num 2.55 4.55 9.55 12.55 10.55 ... +#> $ x3: num 0.194 2.194 4.194 6.194 3.194 ... +#> $ x4: Factor w/ 20 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ... +#> $ x5: Factor w/ 13 levels "c","e","f","g",..: 1 5 3 2 4 7 6 8 9 8 ... +#> $ x6: Factor w/ 15 levels "c","d","f","g",..: 1 2 3 4 3 5 6 7 8 9 ... +#> $ x7: Factor w/ 5 levels "a","b","c","d",..: 2 2 1 4 5 1 4 3 1 5 ... +head(exam) +#> x1 x2 x3 x4 x5 x6 x7 +#> 1 1 2.554297 0.1939687 a c c b +#> 2 2 4.554297 2.1939687 b h d b +#> 3 3 9.554297 4.1939687 c f f a +#> 4 4 12.554297 6.1939687 d e g d +#> 5 5 10.554297 3.1939687 e g f e +#> 6 6 6.554297 10.1939687 f l h a + +# default case +exam_01 <- treatment_corr(exam) +#> * remove variables whose strong correlation (pearson >= 0.8) +#> - remove x1 : with x3 (0.825) +#> * remove variables whose strong correlation (spearman >= 0.8) +#> - remove x4 : with x5 (0.9649) +#> - remove x4 : with x6 (0.9928) +#> - remove x5 : with x6 (0.9485) +head(exam_01) +#> x2 x3 x6 x7 +#> 1 2.554297 0.1939687 c b +#> 2 4.554297 2.1939687 d b +#> 3 9.554297 4.1939687 f a +#> 4 12.554297 6.1939687 g d +#> 5 10.554297 3.1939687 f e +#> 6 6.554297 10.1939687 h a + +# not removing variables +treatment_corr(exam, treat = FALSE) +#> * remove variables whose strong correlation (pearson >= 0.8) +#> - remove x1 : with x3 (0.825) +#> * remove variables whose strong correlation (spearman >= 0.8) +#> - remove x4 : with x5 (0.9649) +#> - remove x4 : with x6 (0.9928) +#> - remove x5 : with x6 (0.9485) + +# Set a threshold to detecting variables when correlation greater then 0.9 +treatment_corr(exam, corr_thres = 0.9, treat = FALSE) +#> * remove variables whose strong correlation (spearman >= 0.9) +#> - remove x4 : with x5 (0.9649) +#> - remove x4 : with x6 (0.9928) +#> - remove x5 : with x6 (0.9485) + +# not verbose mode +exam_02 <- treatment_corr(exam, verbose = FALSE) +head(exam_02) +#> x2 x3 x6 x7 +#> 1 2.554297 0.1939687 c b +#> 2 4.554297 2.1939687 d b +#> 3 9.554297 4.1939687 f a +#> 4 12.554297 6.1939687 g d +#> 5 10.554297 3.1939687 f e +#> 6 6.554297 10.1939687 h a
    • -remove variables whose strong correlation : x1, x4, x5 -are removed.
      • +remove variables whose strong correlation : x1, x4, x5 are removed.
    @@ -478,91 +448,81 @@

    Split the data into a tr

    Data: Credit Card Default Data

    -

    Default of ISLR package is a simulated data -set containing information on ten thousand customers. The aim here is to -predict which customers will default on their credit card debt.

    -

    A data frame with 10000 observations on the following 4 -variables.:

    +

    Default of ISLR package is a simulated data set containing information on ten thousand customers. The aim here is to predict which customers will default on their credit card debt.

    +

    A data frame with 10000 observations on the following 4 variables.:

    • -default : factor. A factor with levels No and Yes -indicating whether the customer defaulted on their debt
      • +default : factor. A factor with levels No and Yes indicating whether the customer defaulted on their debt
    • -student: factor. A factor with levels No and Yes -indicating whether the customer is a student
      • +student: factor. A factor with levels No and Yes indicating whether the customer is a student
    • -balance: numeric. The average balance that the customer -has remaining on their credit card after making their monthly -payment
      • +balance: numeric. The average balance that the customer has remaining on their credit card after making their monthly payment
    • income : numeric. Income of customer
    -# Credit Card Default Data
-head(ISLR::Default)
-#>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
-
-# structure of dataset
-str(ISLR::Default)
-#> 'data.frame':    10000 obs. of  4 variables:
-#>  $ default: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
-#>  $ student: Factor w/ 2 levels "No","Yes": 1 2 1 1 1 2 1 2 1 1 ...
-#>  $ balance: num  730 817 1074 529 786 ...
-#>  $ income : num  44362 12106 31767 35704 38463 ...
-
-# summary of dataset
-summary(ISLR::Default)
-#>  default    student       balance           income     
-#>  No :9667   No :7056   Min.   :   0.0   Min.   :  772  
-#>  Yes: 333   Yes:2944   1st Qu.: 481.7   1st Qu.:21340  
-#>                        Median : 823.6   Median :34553  
-#>                        Mean   : 835.4   Mean   :33517  
-#>                        3rd Qu.:1166.3   3rd Qu.:43808  
-#>                        Max.   :2654.3   Max.   :73554
    +# Credit Card Default Data +head(ISLR::Default) +#> default student balance income +#> 1 No No 729.5265 44361.625 +#> 2 No Yes 817.1804 12106.135 +#> 3 No No 1073.5492 31767.139 +#> 4 No No 529.2506 35704.494 +#> 5 No No 785.6559 38463.496 +#> 6 No Yes 919.5885 7491.559 + +# structure of dataset +str(ISLR::Default) +#> 'data.frame': 10000 obs. of 4 variables: +#> $ default: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ... +#> $ student: Factor w/ 2 levels "No","Yes": 1 2 1 1 1 2 1 2 1 1 ... +#> $ balance: num 730 817 1074 529 786 ... +#> $ income : num 44362 12106 31767 35704 38463 ... + +# summary of dataset +summary(ISLR::Default) +#> default student balance income +#> No :9667 No :7056 Min. : 0.0 Min. : 772 +#> Yes: 333 Yes:2944 1st Qu.: 481.7 1st Qu.:21340 +#> Median : 823.6 Median :34553 +#> Mean : 835.4 Mean :33517 +#> 3rd Qu.:1166.3 3rd Qu.:43808 +#> Max. :2654.3 Max. :73554

    Split dataset

    -

    split_by() splits the data.frame or tbl_df into a -training set and a test set.

    +

    split_by() splits the data.frame or tbl_df into a training set and a test set.

    Split dataset with split_by()

    -

    The split_df class is created, which contains the split -information and criteria to separate the training and the test set.

    +

    The split_df class is created, which contains the split information and criteria to separate the training and the test set.

    -library(alookr)
-library(dplyr)
-
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default, seed = 6534)
-
-sb
-#> # A tibble: 10,000 x 5
-#> # Groups:   split_flag [2]
-#>    default student balance income split_flag
-#>    <fct>   <fct>     <dbl>  <dbl> <chr>     
-#>  1 No      No         730. 44362. train     
-#>  2 No      Yes        817. 12106. train     
-#>  3 No      No        1074. 31767. train     
-#>  4 No      No         529. 35704. train     
-#>  5 No      No         786. 38463. test      
-#>  6 No      Yes        920.  7492. train     
-#>  7 No      No         826. 24905. test      
-#>  8 No      Yes        809. 17600. train     
-#>  9 No      No        1161. 37469. train     
-#> 10 No      No           0  29275. train     
-#> # … with 9,990 more rows
    -

    The attributes of the split_df class are as -follows.:

    +library(alookr) +library(dplyr) + +# Generate data for the example +sb <- ISLR::Default %>% + split_by(default, seed = 6534) + +sb +#> # A tibble: 10,000 x 5 +#> # Groups: split_flag [2] +#> default student balance income split_flag +#> <fct> <fct> <dbl> <dbl> <chr> +#> 1 No No 730. 44362. train +#> 2 No Yes 817. 12106. train +#> 3 No No 1074. 31767. train +#> 4 No No 529. 35704. train +#> 5 No No 786. 38463. test +#> 6 No Yes 920. 7492. train +#> 7 No No 826. 24905. test +#> 8 No Yes 809. 17600. train +#> 9 No No 1161. 37469. train +#> 10 No No 0 29275. train +#> # … with 9,990 more rows
    +

    The attributes of the split_df class are as follows.:

    • split_seed : integer. random seed used for splitting
    • target : character. the name of the target variable
      • @@ -573,435 +533,407 @@

      Split dataset with split_by()
    • majority_rate : numeric. the rate of the majority class

    -attr_names <- names(attributes(sb))
-attr_names
-#>  [1] "names"         "row.names"     "groups"        "class"        
-#>  [5] "split_seed"    "target"        "binary"        "minority"     
-#>  [9] "majority"      "minority_rate" "majority_rate"
-
-sb_attr <- attributes(sb)
-
-# The third property, row.names, is excluded from the output because its length is very long.
-sb_attr[!attr_names %in% "row.names"]
-#> $names
-#> [1] "default"    "student"    "balance"    "income"     "split_flag"
-#> 
-#> $groups
-#> # A tibble: 2 x 2
-#>   split_flag       .rows
-#> * <chr>      <list<int>>
-#> 1 test           [3,000]
-#> 2 train          [7,000]
-#> 
-#> $class
-#> [1] "split_df"   "grouped_df" "tbl_df"     "tbl"        "data.frame"
-#> 
-#> $split_seed
-#> [1] 6534
-#> 
-#> $target
-#>   default 
-#> "default" 
-#> 
-#> $binary
-#> [1] TRUE
-#> 
-#> $minority
-#> [1] "Yes"
-#> 
-#> $majority
-#> [1] "No"
-#> 
-#> $minority_rate
-#>    Yes 
-#> 0.0333 
-#> 
-#> $majority_rate
-#>     No 
-#> 0.9667
    -

    summary() summarizes the information of two datasets -splitted by split_by().

    +attr_names <- names(attributes(sb)) +attr_names +#> [1] "names" "row.names" "groups" "class" +#> [5] "split_seed" "target" "binary" "minority" +#> [9] "majority" "minority_rate" "majority_rate" + +sb_attr <- attributes(sb) + +# The third property, row.names, is excluded from the output because its length is very long. +sb_attr[!attr_names %in% "row.names"] +#> $names +#> [1] "default" "student" "balance" "income" "split_flag" +#> +#> $groups +#> # A tibble: 2 x 2 +#> split_flag .rows +#> * <chr> <list<int>> +#> 1 test [3,000] +#> 2 train [7,000] +#> +#> $class +#> [1] "split_df" "grouped_df" "tbl_df" "tbl" "data.frame" +#> +#> $split_seed +#> [1] 6534 +#> +#> $target +#> default +#> "default" +#> +#> $binary +#> [1] TRUE +#> +#> $minority +#> [1] "Yes" +#> +#> $majority +#> [1] "No" +#> +#> $minority_rate +#> Yes +#> 0.0333 +#> +#> $majority_rate +#> No +#> 0.9667
    +

    summary() summarizes the information of two datasets splitted by split_by().

    -summary(sb)
-#> ** Split train/test set information **
-#>  + random seed        :  6534 
-#>  + split data            
-#>     - train set count :  7000 
-#>     - test set count  :  3000 
-#>  + target variable    :  default 
-#>     - minority class  :  Yes (0.033300)
-#>     - majority class  :  No (0.966700)
    +summary(sb) +#> ** Split train/test set information ** +#> + random seed : 6534 +#> + split data +#> - train set count : 7000 +#> - test set count : 3000 +#> + target variable : default +#> - minority class : Yes (0.033300) +#> - majority class : No (0.966700)

    Compare dataset

    -

    Train data and test data should be similar. If the two datasets are -not similar, the performance of the predictive model may be reduced.

    -

    alookr provides a function to compare the similarity -between train dataset and test dataset.

    -

    If the two data sets are not similar, the train dataset and test -dataset should be splitted again from the original data.

    +

    Train data and test data should be similar. If the two datasets are not similar, the performance of the predictive model may be reduced.

    +

    alookr provides a function to compare the similarity between train dataset and test dataset.

    +

    If the two data sets are not similar, the train dataset and test dataset should be splitted again from the original data.

    -

    Comparison of categorical variables with -compare_target_category() +

    Comparison of categorical variables with compare_target_category()

    -

    Compare the statistics of the categorical variables of the train set -and test set included in the “split_df” class.

    +

    Compare the statistics of the categorical variables of the train set and test set included in the “split_df” class.

    -sb %>%
-  compare_target_category()
-#> # A tibble: 4 x 5
-#>   variable level train  test abs_diff
-#>   <chr>    <fct> <dbl> <dbl>    <dbl>
-#> 1 default  No    96.7  96.7   0.00476
-#> 2 default  Yes    3.33  3.33  0.00476
-#> 3 student  No    70.0  71.8   1.77   
-#> 4 student  Yes   30.0  28.2   1.77
-
-# compare variables that are character data types.
-sb %>%
-  compare_target_category(add_character = TRUE)
-#> # A tibble: 4 x 5
-#>   variable level train  test abs_diff
-#>   <chr>    <fct> <dbl> <dbl>    <dbl>
-#> 1 default  No    96.7  96.7   0.00476
-#> 2 default  Yes    3.33  3.33  0.00476
-#> 3 student  No    70.0  71.8   1.77   
-#> 4 student  Yes   30.0  28.2   1.77
-
-# display marginal
-sb %>%
-  compare_target_category(margin = TRUE)
-#> # A tibble: 6 x 5
-#>   variable level    train   test abs_diff
-#>   <chr>    <fct>    <dbl>  <dbl>    <dbl>
-#> 1 default  No       96.7   96.7   0.00476
-#> 2 default  Yes       3.33   3.33  0.00476
-#> 3 default  <Total> 100    100     0.00952
-#> 4 student  No       70.0   71.8   1.77   
-#> 5 student  Yes      30.0   28.2   1.77   
-#> 6 student  <Total> 100    100     3.54
-
-# student variable only
-sb %>%
-  compare_target_category(student)
-#> # A tibble: 2 x 5
-#>   variable level train  test abs_diff
-#>   <chr>    <fct> <dbl> <dbl>    <dbl>
-#> 1 student  No     70.0  71.8     1.77
-#> 2 student  Yes    30.0  28.2     1.77
-
-sb %>%
-  compare_target_category(student, margin = TRUE)
-#> # A tibble: 3 x 5
-#>   variable level   train  test abs_diff
-#>   <chr>    <fct>   <dbl> <dbl>    <dbl>
-#> 1 student  No       70.0  71.8     1.77
-#> 2 student  Yes      30.0  28.2     1.77
-#> 3 student  <Total> 100   100       3.54
    -

    compare_target_category() returns tbl_df, where the variables have -the following.:

    +sb %>% + compare_target_category() +#> # A tibble: 4 x 5 +#> variable level train test abs_diff +#> <chr> <fct> <dbl> <dbl> <dbl> +#> 1 default No 96.7 96.7 0.00476 +#> 2 default Yes 3.33 3.33 0.00476 +#> 3 student No 70.0 71.8 1.77 +#> 4 student Yes 30.0 28.2 1.77 + +# compare variables that are character data types. +sb %>% + compare_target_category(add_character = TRUE) +#> # A tibble: 4 x 5 +#> variable level train test abs_diff +#> <chr> <fct> <dbl> <dbl> <dbl> +#> 1 default No 96.7 96.7 0.00476 +#> 2 default Yes 3.33 3.33 0.00476 +#> 3 student No 70.0 71.8 1.77 +#> 4 student Yes 30.0 28.2 1.77 + +# display marginal +sb %>% + compare_target_category(margin = TRUE) +#> # A tibble: 6 x 5 +#> variable level train test abs_diff +#> <chr> <fct> <dbl> <dbl> <dbl> +#> 1 default No 96.7 96.7 0.00476 +#> 2 default Yes 3.33 3.33 0.00476 +#> 3 default <Total> 100 100 0.00952 +#> 4 student No 70.0 71.8 1.77 +#> 5 student Yes 30.0 28.2 1.77 +#> 6 student <Total> 100 100 3.54 + +# student variable only +sb %>% + compare_target_category(student) +#> # A tibble: 2 x 5 +#> variable level train test abs_diff +#> <chr> <fct> <dbl> <dbl> <dbl> +#> 1 student No 70.0 71.8 1.77 +#> 2 student Yes 30.0 28.2 1.77 + +sb %>% + compare_target_category(student, margin = TRUE) +#> # A tibble: 3 x 5 +#> variable level train test abs_diff +#> <chr> <fct> <dbl> <dbl> <dbl> +#> 1 student No 70.0 71.8 1.77 +#> 2 student Yes 30.0 28.2 1.77 +#> 3 student <Total> 100 100 3.54
    +

    compare_target_category() returns tbl_df, where the variables have the following.:

    • variable : character. categorical variable name
    • level : factor. level of categorical variables
      • -
    • train : numeric. the relative frequency of the level in the train -set
      • -
    • test : numeric. the relative frequency of the level in the test -set
      • -
    • abs_diff : numeric. the absolute value of the difference between two -relative frequencies
      • +
    • train : numeric. the relative frequency of the level in the train set
      • +
    • test : numeric. the relative frequency of the level in the test set
      • +
    • abs_diff : numeric. the absolute value of the difference between two relative frequencies
    -

    Comparison of numeric variables with -compare_target_numeric() +

    Comparison of numeric variables with compare_target_numeric()

    -

    Compare the statistics of the numerical variables of the train set -and test set included in the “split_df” class.

    +

    Compare the statistics of the numerical variables of the train set and test set included in the “split_df” class.

    -sb %>%
-  compare_target_numeric()
-#> # A tibble: 2 x 7
-#>   variable train_mean test_mean train_sd test_sd train_z test_z
-#>   <chr>         <dbl>     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
-#> 1 balance        836.      834.     487.    477.    1.72   1.75
-#> 2 income       33446.    33684.   13437.  13101.    2.49   2.57
-
-# balance variable only
-sb %>%
-  compare_target_numeric(balance)
-#> # A tibble: 1 x 7
-#>   variable train_mean test_mean train_sd test_sd train_z test_z
-#>   <chr>         <dbl>     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
-#> 1 balance        836.      834.     487.    477.    1.72   1.75
    -

    compare_target_numeric() returns tbl_df, where the variables have the -following.:

    +sb %>% + compare_target_numeric() +#> # A tibble: 2 x 7 +#> variable train_mean test_mean train_sd test_sd train_z test_z +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 balance 836. 834. 487. 477. 1.72 1.75 +#> 2 income 33446. 33684. 13437. 13101. 2.49 2.57 + +# balance variable only +sb %>% + compare_target_numeric(balance) +#> # A tibble: 1 x 7 +#> variable train_mean test_mean train_sd test_sd train_z test_z +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 balance 836. 834. 487. 477. 1.72 1.75
    +

    compare_target_numeric() returns tbl_df, where the variables have the following.:

    • variable : character. numeric variable name
    • train_mean : numeric. arithmetic mean of train set
    • test_mean : numeric. arithmetic mean of test set
    • train_sd : numeric. standard deviation of train set
    • test_sd : numeric. standard deviation of test set
      • -
    • train_z : numeric. the arithmetic mean of the train set divided by -the standard deviation
      • -
    • test_z : numeric. the arithmetic mean of the test set divided by the -standard deviation
      • +
    • train_z : numeric. the arithmetic mean of the train set divided by the standard deviation
      • +
    • test_z : numeric. the arithmetic mean of the test set divided by the standard deviation

    Comparison plot with compare_plot()

    -

    Plot compare information of the train set and test set included in -the “split_df” class.

    +

    Plot compare information of the train set and test set included in the “split_df” class.

    -# income variable only
-sb %>%
-  compare_plot("income")
    +# income variable only +sb %>% + compare_plot("income")

    -# all varibales
-sb %>%
-  compare_plot()
    +# all varibales +sb %>% + compare_plot()

    -

    Diagnosis of train set and test set with -compare_diag() +

    Diagnosis of train set and test set with compare_diag()

    -

    Diagnosis of similarity between datasets splitted by train set and -set included in the “split_df” class.

    +

    Diagnosis of similarity between datasets splitted by train set and set included in the “split_df” class.

    -defaults <- ISLR::Default
-defaults$id <- seq(NROW(defaults))
-
-set.seed(1)
-defaults[sample(seq(NROW(defaults)), 3), "student"] <- NA
-set.seed(2)
-defaults[sample(seq(NROW(defaults)), 10), "balance"] <- NA
-
-sb_2 <- defaults %>%
-  split_by(default)
-
-sb_2 %>%
-  compare_diag()
-#> * Detected diagnose missing value
-#>  - student
-#>  - balance
-#>  - balance
-#> 
-#> * Detected diagnose missing levels
-#>  - student
-#> $missing_value
-#> # A tibble: 3 x 4
-#>   variables train_misscount train_missrate test_missrate
-#>   <chr>               <int>          <dbl>         <dbl>
-#> 1 student                 3         0.0429       NA     
-#> 2 balance                 8         0.114        NA     
-#> 3 balance                 2        NA             0.0667
-#> 
-#> $single_value
-#> # A tibble: 0 x 3
-#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
-#> 
-#> $uniq_rate
-#> # A tibble: 0 x 5
-#> # … with 5 variables: variables <chr>, train_uniqcount <int>,
-#> #   train_uniqrate <dbl>, test_uniqcount <int>, test_uniqrate <dbl>
-#> 
-#> $missing_level
-#> # A tibble: 1 x 4
-#>   variables n_levels train_missing_nlevel test_missing_nlevel
-#>   <chr>        <int>                <int>               <int>
-#> 1 student          3                    0                   1
-
-sb_2 %>%
-  compare_diag(add_character = TRUE)
-#> * Detected diagnose missing value
-#>  - student
-#>  - balance
-#>  - balance
-#> 
-#> * Detected diagnose missing levels
-#>  - student
-#> $missing_value
-#> # A tibble: 3 x 4
-#>   variables train_misscount train_missrate test_missrate
-#>   <chr>               <int>          <dbl>         <dbl>
-#> 1 student                 3         0.0429       NA     
-#> 2 balance                 8         0.114        NA     
-#> 3 balance                 2        NA             0.0667
-#> 
-#> $single_value
-#> # A tibble: 0 x 3
-#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
-#> 
-#> $uniq_rate
-#> # A tibble: 0 x 5
-#> # … with 5 variables: variables <chr>, train_uniqcount <int>,
-#> #   train_uniqrate <dbl>, test_uniqcount <int>, test_uniqrate <dbl>
-#> 
-#> $missing_level
-#> # A tibble: 1 x 4
-#>   variables n_levels train_missing_nlevel test_missing_nlevel
-#>   <chr>        <int>                <int>               <int>
-#> 1 student          3                    0                   1
-
-sb_2 %>%
-  compare_diag(uniq_thres = 0.0005)
-#> * Detected diagnose missing value
-#>  - student
-#>  - balance
-#>  - balance
-#> 
-#> * Detected diagnose many unique value
-#>  - default
-#>  - student
-#> 
-#> * Detected diagnose missing levels
-#>  - student
-#> $missing_value
-#> # A tibble: 3 x 4
-#>   variables train_misscount train_missrate test_missrate
-#>   <chr>               <int>          <dbl>         <dbl>
-#> 1 student                 3         0.0429       NA     
-#> 2 balance                 8         0.114        NA     
-#> 3 balance                 2        NA             0.0667
-#> 
-#> $single_value
-#> # A tibble: 0 x 3
-#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
-#> 
-#> $uniq_rate
-#> # A tibble: 2 x 5
-#>   variables train_uniqcount train_uniqrate test_uniqcount test_uniqrate
-#>   <chr>               <int>          <dbl>          <int>         <dbl>
-#> 1 default                NA             NA              2      0.000667
-#> 2 student                NA             NA              2      0.000667
-#> 
-#> $missing_level
-#> # A tibble: 1 x 4
-#>   variables n_levels train_missing_nlevel test_missing_nlevel
-#>   <chr>        <int>                <int>               <int>
-#> 1 student          3                    0                   1
    +defaults <- ISLR::Default +defaults$id <- seq(NROW(defaults)) + +set.seed(1) +defaults[sample(seq(NROW(defaults)), 3), "student"] <- NA +set.seed(2) +defaults[sample(seq(NROW(defaults)), 10), "balance"] <- NA + +sb_2 <- defaults %>% + split_by(default) + +sb_2 %>% + compare_diag() +#> * Detected diagnose missing value +#> - student +#> - balance +#> - balance +#> +#> * Detected diagnose missing levels +#> - student +#> $missing_value +#> # A tibble: 3 x 4 +#> variables train_misscount train_missrate test_missrate +#> <chr> <int> <dbl> <dbl> +#> 1 student 3 0.0429 NA +#> 2 balance 8 0.114 NA +#> 3 balance 2 NA 0.0667 +#> +#> $single_value +#> # A tibble: 0 x 3 +#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl> +#> +#> $uniq_rate +#> # A tibble: 0 x 5 +#> # … with 5 variables: variables <chr>, train_uniqcount <int>, +#> # train_uniqrate <dbl>, test_uniqcount <int>, test_uniqrate <dbl> +#> +#> $missing_level +#> # A tibble: 1 x 4 +#> variables n_levels train_missing_nlevel test_missing_nlevel +#> <chr> <int> <int> <int> +#> 1 student 3 0 1 + +sb_2 %>% + compare_diag(add_character = TRUE) +#> * Detected diagnose missing value +#> - student +#> - balance +#> - balance +#> +#> * Detected diagnose missing levels +#> - student +#> $missing_value +#> # A tibble: 3 x 4 +#> variables train_misscount train_missrate test_missrate +#> <chr> <int> <dbl> <dbl> +#> 1 student 3 0.0429 NA +#> 2 balance 8 0.114 NA +#> 3 balance 2 NA 0.0667 +#> +#> $single_value +#> # A tibble: 0 x 3 +#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl> +#> +#> $uniq_rate +#> # A tibble: 0 x 5 +#> # … with 5 variables: variables <chr>, train_uniqcount <int>, +#> # train_uniqrate <dbl>, test_uniqcount <int>, test_uniqrate <dbl> +#> +#> $missing_level +#> # A tibble: 1 x 4 +#> variables n_levels train_missing_nlevel test_missing_nlevel +#> <chr> <int> <int> <int> +#> 1 student 3 0 1 + +sb_2 %>% + compare_diag(uniq_thres = 0.0005) +#> * Detected diagnose missing value +#> - student +#> - balance +#> - balance +#> +#> * Detected diagnose many unique value +#> - default +#> - student +#> +#> * Detected diagnose missing levels +#> - student +#> $missing_value +#> # A tibble: 3 x 4 +#> variables train_misscount train_missrate test_missrate +#> <chr> <int> <dbl> <dbl> +#> 1 student 3 0.0429 NA +#> 2 balance 8 0.114 NA +#> 3 balance 2 NA 0.0667 +#> +#> $single_value +#> # A tibble: 0 x 3 +#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl> +#> +#> $uniq_rate +#> # A tibble: 2 x 5 +#> variables train_uniqcount train_uniqrate test_uniqcount test_uniqrate +#> <chr> <int> <dbl> <int> <dbl> +#> 1 default NA NA 2 0.000667 +#> 2 student NA NA 2 0.000667 +#> +#> $missing_level +#> # A tibble: 1 x 4 +#> variables n_levels train_missing_nlevel test_missing_nlevel +#> <chr> <int> <int> <int> +#> 1 student 3 0 1

    Extract train/test dataset

    -

    If you compare the train set with the test set and find that the two -datasets are similar, extract the data from the split_df object.

    +

    If you compare the train set with the test set and find that the two datasets are similar, extract the data from the split_df object.

    Extract train set or test set with extract_set()

    Extract train set or test set from split_df class object.

    -train <- sb %>%
-  extract_set(set = "train")
-
-test <- sb %>%
-  extract_set(set = "test")
-
-dim(train)
-#> [1] 7000    4
-
-dim(test)
-#> [1] 3000    4
    +train <- sb %>% + extract_set(set = "train") + +test <- sb %>% + extract_set(set = "test") + +dim(train) +#> [1] 7000 4 + +dim(test) +#> [1] 3000 4
    -

    Extract the data to fit the model with -sampling_target() +

    Extract the data to fit the model with sampling_target()

    -

    In a target class, the ratio of the majority class to the minority -class is not similar and the ratio of the minority class is very small, -which is called the imbalanced class.

    -

    If target variable is an imbalanced class, the characteristics of the -majority class are actively reflected in the model. This model implies -an error in predicting the minority class as the majority class. So we -have to make the train dataset a balanced class.

    -

    sampling_target() performs sampling on the train set of -split_df to resolve the imbalanced class.

    +

    In a target class, the ratio of the majority class to the minority class is not similar and the ratio of the minority class is very small, which is called the imbalanced class.

    +

    If target variable is an imbalanced class, the characteristics of the majority class are actively reflected in the model. This model implies an error in predicting the minority class as the majority class. So we have to make the train dataset a balanced class.

    +

    sampling_target() performs sampling on the train set of split_df to resolve the imbalanced class.

    -# under-sampling with random seed
-under <- sb %>%
-  sampling_target(seed = 1234L)
-
-under %>%
-  count(default)
-#> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No        233
-#> 2 Yes       233
-
-# under-sampling with random seed, and minority class frequency is 40%
-under40 <- sb %>%
-  sampling_target(seed = 1234L, perc = 40)
-
-under40 %>%
-  count(default)
-#> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No        349
-#> 2 Yes       233
-
-# over-sampling with random seed
-over <- sb %>%
-  sampling_target(method = "ubOver", seed = 1234L)
-
-over %>%
-  count(default)
-#> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No       6767
-#> 2 Yes      6767
-
-# over-sampling with random seed, and k = 10
-over10 <- sb %>%
-  sampling_target(method = "ubOver", seed = 1234L, k = 10)
-
-over10 %>%
-  count(default)
-#> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No       6767
-#> 2 Yes      2330
-
-# SMOTE with random seed
-smote <- sb %>%
-  sampling_target(method = "ubSMOTE", seed = 1234L)
-
-smote %>%
-  count(default)
-#> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No        932
-#> 2 Yes       699
-
-# SMOTE with random seed, and perc.under = 250
-smote250 <- sb %>%
-  sampling_target(method = "ubSMOTE", seed = 1234L, perc.under = 250)
-
-smote250 %>%
-  count(default)
-#> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No       1165
-#> 2 Yes       699
    -

    The argument that specifies the sampling method in sampling_target () -is method. “ubUnder” is under-sampling, and “ubOver” is over-sampling, -“ubSMOTE” is SMOTE(Synthetic Minority Over-sampling TEchnique).

    +# under-sampling with random seed +under <- sb %>% + sampling_target(seed = 1234L) + +under %>% + count(default) +#> # A tibble: 2 x 2 +#> default n +#> <fct> <int> +#> 1 No 233 +#> 2 Yes 233 + +# under-sampling with random seed, and minority class frequency is 40% +under40 <- sb %>% + sampling_target(seed = 1234L, perc = 40) + +under40 %>% + count(default) +#> # A tibble: 2 x 2 +#> default n +#> <fct> <int> +#> 1 No 349 +#> 2 Yes 233 + +# over-sampling with random seed +over <- sb %>% + sampling_target(method = "ubOver", seed = 1234L) + +over %>% + count(default) +#> # A tibble: 2 x 2 +#> default n +#> <fct> <int> +#> 1 No 6767 +#> 2 Yes 6767 + +# over-sampling with random seed, and k = 10 +over10 <- sb %>% + sampling_target(method = "ubOver", seed = 1234L, k = 10) + +over10 %>% + count(default) +#> # A tibble: 2 x 2 +#> default n +#> <fct> <int> +#> 1 No 6767 +#> 2 Yes 2330 + +# SMOTE with random seed +smote <- sb %>% + sampling_target(method = "ubSMOTE", seed = 1234L) + +smote %>% + count(default) +#> # A tibble: 2 x 2 +#> default n +#> <fct> <int> +#> 1 No 932 +#> 2 Yes 699 + +# SMOTE with random seed, and perc.under = 250 +smote250 <- sb %>% + sampling_target(method = "ubSMOTE", seed = 1234L, perc.under = 250) + +smote250 %>% + count(default) +#> # A tibble: 2 x 2 +#> default n +#> <fct> <int> +#> 1 No 1165 +#> 2 Yes 699
    +

    The argument that specifies the sampling method in sampling_target () is method. “ubUnder” is under-sampling, and “ubOver” is over-sampling, “ubSMOTE” is SMOTE(Synthetic Minority Over-sampling TEchnique).

    @@ -1011,27 +943,21 @@

    Modeling and Evaluate, Predict

    Data: Wisconsin Breast Cancer Data

    -

    BreastCancer of mlbench package is a breast -cancer data. The objective is to identify each of a number of benign or -malignant classes.

    -

    A data frame with 699 observations on 11 variables, one being a -character variable, 9 being ordered or nominal, and 1 target class.:

    +

    BreastCancer of mlbench package is a breast cancer data. The objective is to identify each of a number of benign or malignant classes.

    +

    A data frame with 699 observations on 11 variables, one being a character variable, 9 being ordered or nominal, and 1 target class.:

    • Id : character. Sample code number
    • Cl.thickness : ordered factor. Clump Thickness
    • -Cell.size : ordered factor. Uniformity of Cell -Size
      • +Cell.size : ordered factor. Uniformity of Cell Size
    • -Cell.shape : ordered factor. Uniformity of Cell -Shape
      • +Cell.shape : ordered factor. Uniformity of Cell Shape
    • Marg.adhesion : ordered factor. Marginal Adhesion
    • -Epith.c.size : ordered factor. Single Epithelial Cell -Size
      • +Epith.c.size : ordered factor. Single Epithelial Cell Size
    • Bare.nuclei : factor. Bare Nuclei
    • @@ -1041,21 +967,20 @@

      Data: Wisconsin Breast Cancer Data Mitoses : factor. Mitoses

    • -Class : factor. Class. level is benign and -malignant.
      • +Class : factor. Class. level is benign and malignant. 
    -library(mlbench)
-data(BreastCancer)
-
-# class of each variables
-sapply(BreastCancer, function(x) class(x)[1])
-#>              Id    Cl.thickness       Cell.size      Cell.shape   Marg.adhesion 
-#>     "character"       "ordered"       "ordered"       "ordered"       "ordered" 
-#>    Epith.c.size     Bare.nuclei     Bl.cromatin Normal.nucleoli         Mitoses 
-#>       "ordered"        "factor"        "factor"        "factor"        "factor" 
-#>           Class 
-#>        "factor"
    +library(mlbench) +data(BreastCancer) + +# class of each variables +sapply(BreastCancer, function(x) class(x)[1]) +#> Id Cl.thickness Cell.size Cell.shape Marg.adhesion +#> "character" "ordered" "ordered" "ordered" "ordered" +#> Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses +#> "ordered" "factor" "factor" "factor" "factor" +#> Class +#> "factor"

    Preperation the data @@ -1064,66 +989,59 @@

    Preperation the data

    Fix the missing value with dlookr::imputate_na()

    -

    find the variables that include missing value. and imputate the -missing value using imputate_na() in dlookr package.

    +

    find the variables that include missing value. and imputate the missing value using imputate_na() in dlookr package.

    -library(dlookr)
-library(dplyr)
-
-# variable that have a missing value
-diagnose(BreastCancer) %>%
-  filter(missing_count > 0)
-#> # A tibble: 1 x 6
-#>   variables   types  missing_count missing_percent unique_count unique_rate
-#>   <chr>       <chr>          <int>           <dbl>        <int>       <dbl>
-#> 1 Bare.nuclei factor            16            2.29           11      0.0157
-
-# imputation of missing value
-breastCancer <- BreastCancer %>%
-  mutate(Bare.nuclei = imputate_na(BreastCancer, Bare.nuclei, Class,
-                         method = "mice", no_attrs = TRUE, print_flag = FALSE))
    +library(dlookr) +library(dplyr) + +# variable that have a missing value +diagnose(BreastCancer) %>% + filter(missing_count > 0) +#> # A tibble: 1 x 6 +#> variables types missing_count missing_percent unique_count unique_rate +#> <chr> <chr> <int> <dbl> <int> <dbl> +#> 1 Bare.nuclei factor 16 2.29 11 0.0157 + +# imputation of missing value +breastCancer <- BreastCancer %>% + mutate(Bare.nuclei = imputate_na(BreastCancer, Bare.nuclei, Class, + method = "mice", no_attrs = TRUE, print_flag = FALSE))

    Split data set

    -

    Splits the dataset into a train set and a test set with -split_by() +

    Splits the dataset into a train set and a test set with split_by()

    -

    split_by() in the alookr package splits the dataset into -a train set and a test set.

    -

    The ratio argument of the split_by() function specifies -the ratio of the train set.

    +

    split_by() in the alookr package splits the dataset into a train set and a test set.

    +

    The ratio argument of the split_by() function specifies the ratio of the train set.

    split_by() creates a class object named split_df.

    -library(alookr)
-
-# split the data into a train set and a test set by default arguments
-sb <- breastCancer %>%
-  split_by(target = Class)
-
-# show the class name
-class(sb)
-#> [1] "split_df"   "grouped_df" "tbl_df"     "tbl"        "data.frame"
-
-# split the data into a train set and a test set by ratio = 0.6
-tmp <- breastCancer %>%
-  split_by(Class, ratio = 0.6)
    -

    The summary() function displays the following useful -information about the split_df object:

    +library(alookr) + +# split the data into a train set and a test set by default arguments +sb <- breastCancer %>% + split_by(target = Class) + +# show the class name +class(sb) +#> [1] "split_df" "grouped_df" "tbl_df" "tbl" "data.frame" + +# split the data into a train set and a test set by ratio = 0.6 +tmp <- breastCancer %>% + split_by(Class, ratio = 0.6)
    +

    The summary() function displays the following useful information about the split_df object:

      -
    • random seed : The random seed is the random seed used internally to -separate the data
      • +
    • random seed : The random seed is the random seed used internally to separate the data
    • split data : Information of splited data
      • train set count : number of train set
        • @@ -1132,376 +1050,309 @@

        Splits
      • target variable : Target variable name
          -
        • minority class : name and ratio(In parentheses) of minority -class
          • -
        • majority class : name and ratio(In parentheses) of majority -class
          • +
        • minority class : name and ratio(In parentheses) of minority class
          • +
        • majority class : name and ratio(In parentheses) of majority class

      -# summary() display the some information
-summary(sb)
-#> ** Split train/test set information **
-#>  + random seed        :  2725 
-#>  + split data            
-#>     - train set count :  489 
-#>     - test set count  :  210 
-#>  + target variable    :  Class 
-#>     - minority class  :  malignant (0.344778)
-#>     - majority class  :  benign (0.655222)
-
-# summary() display the some information
-summary(tmp)
-#> ** Split train/test set information **
-#>  + random seed        :  16653 
-#>  + split data            
-#>     - train set count :  419 
-#>     - test set count  :  280 
-#>  + target variable    :  Class 
-#>     - minority class  :  malignant (0.344778)
-#>     - majority class  :  benign (0.655222)
      +# summary() display the some information +summary(sb) +#> ** Split train/test set information ** +#> + random seed : 2725 +#> + split data +#> - train set count : 489 +#> - test set count : 210 +#> + target variable : Class +#> - minority class : malignant (0.344778) +#> - majority class : benign (0.655222) + +# summary() display the some information +summary(tmp) +#> ** Split train/test set information ** +#> + random seed : 16653 +#> + split data +#> - train set count : 419 +#> - test set count : 280 +#> + target variable : Class +#> - minority class : malignant (0.344778) +#> - majority class : benign (0.655222)

    Check missing levels in the train set

    -

    In the case of categorical variables, when a train set and a test set -are separated, a specific level may be missing from the train set.

    -

    In this case, there is no problem when fitting the model, but an -error occurs when predicting with the model you created. Therefore, -preprocessing is performed to avoid missing data preprocessing.

    -

    In the following example, fortunately, there is no categorical -variable that contains the missing levels in the train set.

    +

    In the case of categorical variables, when a train set and a test set are separated, a specific level may be missing from the train set.

    +

    In this case, there is no problem when fitting the model, but an error occurs when predicting with the model you created. Therefore, preprocessing is performed to avoid missing data preprocessing.

    +

    In the following example, fortunately, there is no categorical variable that contains the missing levels in the train set.

    -# list of categorical variables in the train set that contain missing levels
-nolevel_in_train <- sb %>%
-  compare_target_category() %>% 
-  filter(is.na(train)) %>% 
-  select(variable) %>% 
-  unique() %>% 
-  pull
-
-nolevel_in_train
-#> character(0)
-
-# if any of the categorical variables in the train set contain a missing level, 
-# split them again.
-while (length(nolevel_in_train) > 0) {
-  sb <- breastCancer %>%
-    split_by(Class)
-
-  nolevel_in_train <- sb %>%
-    compare_target_category() %>% 
-    filter(is.na(train)) %>% 
-    select(variable) %>% 
-    unique() %>% 
-    pull
-}
    +# list of categorical variables in the train set that contain missing levels +nolevel_in_train <- sb %>% + compare_target_category() %>% + filter(is.na(train)) %>% + select(variable) %>% + unique() %>% + pull + +nolevel_in_train +#> character(0) + +# if any of the categorical variables in the train set contain a missing level, +# split them again. +while (length(nolevel_in_train) > 0) { + sb <- breastCancer %>% + split_by(Class) + + nolevel_in_train <- sb %>% + compare_target_category() %>% + filter(is.na(train)) %>% + select(variable) %>% + unique() %>% + pull +}
    -

    Handling the imbalanced classes data with -sampling_target() +

    Handling the imbalanced classes data with sampling_target()

    Issue of imbalanced classes data

    -

    Imbalanced classes(levels) data means that the number of one level of -the frequency of the target variable is relatively small. In general, -the proportion of positive classes is relatively small. For example, in -the model of predicting spam, the class of interest spam is less than -non-spam.

    -

    Imbalanced classes data is a common problem in machine learning -classification.

    -

    table() and prop.table() are traditionally -useful functions for diagnosing imbalanced classes data. However, -alookr’s summary() is simpler and provides more -information.

    +

    Imbalanced classes(levels) data means that the number of one level of the frequency of the target variable is relatively small. In general, the proportion of positive classes is relatively small. For example, in the model of predicting spam, the class of interest spam is less than non-spam.

    +

    Imbalanced classes data is a common problem in machine learning classification.

    +

    table() and prop.table() are traditionally useful functions for diagnosing imbalanced classes data. However, alookr’s summary() is simpler and provides more information.

    -# train set frequency table - imbalanced classes data
-table(sb$Class)
-#> 
-#>    benign malignant 
-#>       458       241
-
-# train set relative frequency table - imbalanced classes data
-prop.table(table(sb$Class))
-#> 
-#>    benign malignant 
-#> 0.6552217 0.3447783
-
-# using summary function - imbalanced classes data
-summary(sb)
-#> ** Split train/test set information **
-#>  + random seed        :  2725 
-#>  + split data            
-#>     - train set count :  489 
-#>     - test set count  :  210 
-#>  + target variable    :  Class 
-#>     - minority class  :  malignant (0.344778)
-#>     - majority class  :  benign (0.655222)
    +# train set frequency table - imbalanced classes data +table(sb$Class) +#> +#> benign malignant +#> 458 241 + +# train set relative frequency table - imbalanced classes data +prop.table(table(sb$Class)) +#> +#> benign malignant +#> 0.6552217 0.3447783 + +# using summary function - imbalanced classes data +summary(sb) +#> ** Split train/test set information ** +#> + random seed : 2725 +#> + split data +#> - train set count : 489 +#> - test set count : 210 +#> + target variable : Class +#> - minority class : malignant (0.344778) +#> - majority class : benign (0.655222)

    Handling the imbalanced classes data

    -

    Most machine learning algorithms work best when the number of samples -in each class are about equal. And most algorithms are designed to -maximize accuracy and reduce error. So, we requre handling an imbalanced -class problem.

    -

    sampling_target() performs sampling to solve an imbalanced classes -data problem.

    +

    Most machine learning algorithms work best when the number of samples in each class are about equal. And most algorithms are designed to maximize accuracy and reduce error. So, we requre handling an imbalanced class problem.

    +

    sampling_target() performs sampling to solve an imbalanced classes data problem.

    Resampling - oversample minority class

    -

    Oversampling can be defined as adding more copies of the minority -class.

    -

    Oversampling is performed by specifying “ubOver” in the method -argument of the sampling_target() function.

    +

    Oversampling can be defined as adding more copies of the minority class.

    +

    Oversampling is performed by specifying “ubOver” in the method argument of the sampling_target() function.

    -# to balanced by over sampling
-train_over <- sb %>%
-  sampling_target(method = "ubOver")
-
-# frequency table 
-table(train_over$Class)
-#> 
-#>    benign malignant 
-#>       320       320
    +# to balanced by over sampling +train_over <- sb %>% + sampling_target(method = "ubOver") + +# frequency table +table(train_over$Class) +#> +#> benign malignant +#> 320 320

    Resampling - undersample majority class

    -

    Undersampling can be defined as removing some observations of the -majority class.

    -

    Undersampling is performed by specifying “ubUnder” in the method -argument of the sampling_target() function.

    +

    Undersampling can be defined as removing some observations of the majority class.

    +

    Undersampling is performed by specifying “ubUnder” in the method argument of the sampling_target() function.

    -# to balanced by under sampling
-train_under <- sb %>%
-  sampling_target(method = "ubUnder")
-
-# frequency table 
-table(train_under$Class)
-#> 
-#>    benign malignant 
-#>       169       169
    +# to balanced by under sampling +train_under <- sb %>% + sampling_target(method = "ubUnder") + +# frequency table +table(train_under$Class) +#> +#> benign malignant +#> 169 169

    Generate synthetic samples - SMOTE

    -

    SMOTE(Synthetic Minority Oversampling Technique) uses a nearest -neighbors algorithm to generate new and synthetic data.

    -

    SMOTE is performed by specifying “ubSMOTE” in the method argument of -the sampling_target() function.

    +

    SMOTE(Synthetic Minority Oversampling Technique) uses a nearest neighbors algorithm to generate new and synthetic data.

    +

    SMOTE is performed by specifying “ubSMOTE” in the method argument of the sampling_target() function.

    -# to balanced by SMOTE
-train_smote <- sb %>%
-  sampling_target(seed = 1234L, method = "ubSMOTE")
-
-# frequency table 
-table(train_smote$Class)
-#> 
-#>    benign malignant 
-#>       676       507
    +# to balanced by SMOTE +train_smote <- sb %>% + sampling_target(seed = 1234L, method = "ubSMOTE") + +# frequency table +table(train_smote$Class) +#> +#> benign malignant +#> 676 507
    -

    Cleansing the dataset for classification modeling with -cleanse() +

    Cleansing the dataset for classification modeling with cleanse()

    -

    The cleanse() cleanse the dataset for classification -modeling.

    -

    This function is useful when fit the classification model. This -function does the following.:

    +

    The cleanse() cleanse the dataset for classification modeling.

    +

    This function is useful when fit the classification model. This function does the following.:

    • Remove the variable with only one value.
      • -
    • And remove variables that have a unique number of values relative to -the number of observations for a character or categorical variable. +
    • And remove variables that have a unique number of values relative to the number of observations for a character or categorical variable.
        -
      • In this case, it is a variable that corresponds to an identifier or -an identifier.
        • +
      • In this case, it is a variable that corresponds to an identifier or an identifier.
    • And converts the character to factor.
    -

    In this example, The cleanse() function removed a -variable ID with a high unique rate.

    +

    In this example, The cleanse() function removed a variable ID with a high unique rate.

    -# clean the training set
-train <- train_smote %>%
-  cleanse
-#> ── Checking unique value ─────────────────────────── unique value is one ──
-#> No variables that unique value is one.
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
-#> remove variables with high unique rate
-#> ● Id = 425(0.359256128486898)
-#> 
-#> ── Checking character variables ─────────────────────── categorical data ──
-#> No character variables.
    +# clean the training set +train <- train_smote %>% + cleanse +#> ── Checking unique value ─────────────────────────── unique value is one ── +#> No variables that unique value is one. +#> +#> ── Checking unique rate ─────────────────────────────── high unique rate ── +#> remove variables with high unique rate +#> ● Id = 425(0.359256128486898) +#> +#> ── Checking character variables ─────────────────────── categorical data ── +#> No character variables.
    -

    Extract test set for evaluation of the model with -extract_set() +

    Extract test set for evaluation of the model with extract_set() 

    -# extract test set
-test <- sb %>%
-  extract_set(set = "test")
    +# extract test set +test <- sb %>% + extract_set(set = "test")

    Binary classification modeling with run_models()

    -

    run_models() performs some representative binary -classification modeling using split_df object created by -split_by().

    -

    run_models() executes the process in parallel when -fitting the model. However, it is not supported in MS-Windows operating -system and RStudio environment.

    +

    run_models() performs some representative binary classification modeling using split_df object created by split_by().

    +

    run_models() executes the process in parallel when fitting the model. However, it is not supported in MS-Windows operating system and RStudio environment.

    Currently supported algorithms are as follows.:

    • logistic : logistic regression using stats package
      • -
    • rpart : Recursive Partitioning Trees using rpart -package
      • -
    • ctree : Conditional Inference Trees using party -package
      • -
    • randomForest :Classification with Random Forest using -randomForest package
      • -
    • ranger : A Fast Implementation of Random Forests using -ranger package
      • +
    • rpart : Recursive Partitioning Trees using rpart package
      • +
    • ctree : Conditional Inference Trees using party package
      • +
    • randomForest :Classification with Random Forest using randomForest package
      • +
    • ranger : A Fast Implementation of Random Forests using ranger package
    -

    run_models() returns a model_df class -object.

    -

    The model_df class object contains the following -variables.:

    +

    run_models() returns a model_df class object.

    +

    The model_df class object contains the following variables.:

      -
    • step : character. The current stage in the classification modeling -process. +
    • step : character. The current stage in the classification modeling process.
        -
      • For objects created with run_models(), the value of the -variable is “1.Fitted”.
        • +
      • For objects created with run_models(), the value of the variable is “1.Fitted”.
    • model_id : model identifiers
    • target : name of target variable
    • positive : positive class in target variable
      • -
    • fitted_model : list. Fitted model object by model_id’s -algorithms
      • +
    • fitted_model : list. Fitted model object by model_id’s algorithms
    -result <- train %>% 
-  run_models(target = "Class", positive = "malignant")
-result
-#> # A tibble: 6 x 7
-#>   step     model_id     target is_factor positive  negative fitted_model
-#>   <chr>    <chr>        <chr>  <lgl>     <chr>     <chr>    <list>      
-#> 1 1.Fitted logistic     Class  TRUE      malignant benign   <glm>       
-#> 2 1.Fitted rpart        Class  TRUE      malignant benign   <rpart>     
-#> 3 1.Fitted ctree        Class  TRUE      malignant benign   <BinaryTr>  
-#> 4 1.Fitted randomForest Class  TRUE      malignant benign   <rndmFrs.>  
-#> 5 1.Fitted ranger       Class  TRUE      malignant benign   <ranger>    
-#> 6 1.Fitted xgboost      Class  TRUE      malignant benign   <xgb.Bstr>
    +result <- train %>% + run_models(target = "Class", positive = "malignant") +result +#> # A tibble: 6 x 7 +#> step model_id target is_factor positive negative fitted_model +#> <chr> <chr> <chr> <lgl> <chr> <chr> <list> +#> 1 1.Fitted logistic Class TRUE malignant benign <glm> +#> 2 1.Fitted rpart Class TRUE malignant benign <rpart> +#> 3 1.Fitted ctree Class TRUE malignant benign <BinaryTr> +#> 4 1.Fitted randomForest Class TRUE malignant benign <rndmFrs.> +#> 5 1.Fitted ranger Class TRUE malignant benign <ranger> +#> 6 1.Fitted xgboost Class TRUE malignant benign <xgb.Bstr>

    Evaluate the model

    Evaluate the predictive performance of fitted models.

    -

    Predict test set using fitted model with -run_predict() +

    Predict test set using fitted model with run_predict()

    -

    run_predict() predict the test set using -model_df class fitted by run_models().

    -

    run_predict () is executed in parallel when predicting -by model. However, it is not supported in MS-Windows operating system -and RStudio environment.

    -

    The model_df class object contains the following -variables.:

    +

    run_predict() predict the test set using model_df class fitted by run_models().

    +

    run_predict () is executed in parallel when predicting by model. However, it is not supported in MS-Windows operating system and RStudio environment.

    +

    The model_df class object contains the following variables.:

      -
    • step : character. The current stage in the classification modeling -process. +
    • step : character. The current stage in the classification modeling process.
        -
      • For objects created with run_predict(), the value of -the variable is “2.Predicted”.
        • +
      • For objects created with run_predict(), the value of the variable is “2.Predicted”.
    • model_id : character. Type of fit model.
    • target : character. Name of target variable.
      • -
    • positive : character. Level of positive class of binary -classification.
      • -
    • fitted_model : list. Fitted model object by model_id’s -algorithms.
      • +
    • positive : character. Level of positive class of binary classification.
      • +
    • fitted_model : list. Fitted model object by model_id’s algorithms.
    • predicted : result of predcit by each models
    -pred <- result %>%
-  run_predict(test)
-pred
-#> # A tibble: 6 x 8
-#>   step     model_id   target is_factor positive  negative fitted_model predicted
-#>   <chr>    <chr>      <chr>  <lgl>     <chr>     <chr>    <list>       <list>   
-#> 1 2.Predi… logistic   Class  TRUE      malignant benign   <glm>        <fct [21…
-#> 2 2.Predi… rpart      Class  TRUE      malignant benign   <rpart>      <fct [21…
-#> 3 2.Predi… ctree      Class  TRUE      malignant benign   <BinaryTr>   <fct [21…
-#> 4 2.Predi… randomFor… Class  TRUE      malignant benign   <rndmFrs.>   <fct [21…
-#> 5 2.Predi… ranger     Class  TRUE      malignant benign   <ranger>     <fct [21…
-#> 6 2.Predi… xgboost    Class  TRUE      malignant benign   <xgb.Bstr>   <fct [21…
    +pred <- result %>% + run_predict(test) +pred +#> # A tibble: 6 x 8 +#> step model_id target is_factor positive negative fitted_model predicted +#> <chr> <chr> <chr> <lgl> <chr> <chr> <list> <list> +#> 1 2.Predi… logistic Class TRUE malignant benign <glm> <fct [21… +#> 2 2.Predi… rpart Class TRUE malignant benign <rpart> <fct [21… +#> 3 2.Predi… ctree Class TRUE malignant benign <BinaryTr> <fct [21… +#> 4 2.Predi… randomFor… Class TRUE malignant benign <rndmFrs.> <fct [21… +#> 5 2.Predi… ranger Class TRUE malignant benign <ranger> <fct [21… +#> 6 2.Predi… xgboost Class TRUE malignant benign <xgb.Bstr> <fct [21…
    -

    Calculate the performance metric with -run_performance() +

    Calculate the performance metric with run_performance()

    -

    run_performance() calculate the performance metric of -model_df class predicted by run_predict().

    -

    run_performance () is performed in parallel when -calculating the performance evaluation index. However, it is not -supported in MS-Windows operating system and RStudio environment.

    -

    The model_df class object contains the following -variables.:

    +

    run_performance() calculate the performance metric of model_df class predicted by run_predict().

    +

    run_performance () is performed in parallel when calculating the performance evaluation index. However, it is not supported in MS-Windows operating system and RStudio environment.

    +

    The model_df class object contains the following variables.:

      -
    • step : character. The current stage in the classification modeling -process. +
    • step : character. The current stage in the classification modeling process.
        -
      • For objects created with run_performance(), the value -of the variable is “3.Performanced”.
        • +
      • For objects created with run_performance(), the value of the variable is “3.Performanced”.
    • model_id : character. Type of fit model.
    • target : character. Name of target variable.
      • -
    • positive : character. Level of positive class of binary -classification.
      • -
    • fitted_model : list. Fitted model object by model_id’s -algorithms
      • -
    • predicted : list. Predicted value by individual model. Each value -has a predict_class class object.
      • -
    • performance : list. Calculate metrics by individual model. Each -value has a numeric vector.
      • +
    • positive : character. Level of positive class of binary classification.
      • +
    • fitted_model : list. Fitted model object by model_id’s algorithms
      • +
    • predicted : list. Predicted value by individual model. Each value has a predict_class class object.
      • +
    • performance : list. Calculate metrics by individual model. Each value has a numeric vector.
    -# Calculate performace metrics.
-perf <- run_performance(pred)
-perf
-#> # A tibble: 6 x 7
-#>   step          model_id     target positive fitted_model predicted  performance
-#>   <chr>         <chr>        <chr>  <chr>    <list>       <list>     <list>     
-#> 1 3.Performanc… logistic     Class  maligna… <glm>        <fct [210… <dbl [15]> 
-#> 2 3.Performanc… rpart        Class  maligna… <rpart>      <fct [210… <dbl [15]> 
-#> 3 3.Performanc… ctree        Class  maligna… <BinaryTr>   <fct [210… <dbl [15]> 
-#> 4 3.Performanc… randomForest Class  maligna… <rndmFrs.>   <fct [210… <dbl [15]> 
-#> 5 3.Performanc… ranger       Class  maligna… <ranger>     <fct [210… <dbl [15]> 
-#> 6 3.Performanc… xgboost      Class  maligna… <xgb.Bstr>   <fct [210… <dbl [15]>
    -

    The performance variable contains a list object, which contains 15 -performance metrics:

    +# Calculate performace metrics. +perf <- run_performance(pred) +perf +#> # A tibble: 6 x 7 +#> step model_id target positive fitted_model predicted performance +#> <chr> <chr> <chr> <chr> <list> <list> <list> +#> 1 3.Performanc… logistic Class maligna… <glm> <fct [210… <dbl [15]> +#> 2 3.Performanc… rpart Class maligna… <rpart> <fct [210… <dbl [15]> +#> 3 3.Performanc… ctree Class maligna… <BinaryTr> <fct [210… <dbl [15]> +#> 4 3.Performanc… randomForest Class maligna… <rndmFrs.> <fct [210… <dbl [15]> +#> 5 3.Performanc… ranger Class maligna… <ranger> <fct [210… <dbl [15]> +#> 6 3.Performanc… xgboost Class maligna… <xgb.Bstr> <fct [210… <dbl [15]>
    +

    The performance variable contains a list object, which contains 15 performance metrics:

      -
    • ZeroOneLoss : Normalized Zero-One Loss(Classification Error -Loss).
      • +
    • ZeroOneLoss : Normalized Zero-One Loss(Classification Error Loss).
    • Accuracy : Accuracy.
    • Precision : Precision.
    • Recall : Recall.
      • @@ -1510,8 +1361,7 @@

      Calculate the per
    • F1_Score : F1 Score.
    • Fbeta_Score : F-Beta Score.
    • LogLoss : Log loss / Cross-Entropy Loss.
      • -
    • AUC : Area Under the Receiver Operating Characteristic Curve (ROC -AUC).
      • +
    • AUC : Area Under the Receiver Operating Characteristic Curve (ROC AUC).
    • Gini : Gini Coefficient.
    • PRAUC : Area Under the Precision-Recall Curve (PR AUC).
    • LiftAUC : Area Under the Lift Chart.
      • @@ -1519,143 +1369,136 @@

      Calculate the per
    • KS_Stat : Kolmogorov-Smirnov Statistic.

    -# Performance by analytics models
-performance <- perf$performance
-names(performance) <- perf$model_id
-performance
-#> $logistic
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08571429  0.91428571  0.86486486  0.88888889  0.88888889  0.92753623 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.87671233  0.87671233  2.76028551  0.91339573  0.86714976  0.10267867 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.14456141  0.77166005 83.03140097 
-#> 
-#> $rpart
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08571429  0.91428571  0.85526316  0.90277778  0.90277778  0.92028986 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.87837838  0.87837838  0.76993613  0.92200081  0.86090982  0.76243464 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.91998633  0.77731481 83.63526570 
-#> 
-#> $ctree
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08571429  0.91428571  0.83750000  0.93055556  0.93055556  0.90579710 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.88157895  0.88157895  0.99432388  0.94509863  0.90438808  0.71643789 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.85328656  0.79249339 86.35265700 
-#> 
-#> $randomForest
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.05238095  0.94761905  0.89610390  0.95833333  0.95833333  0.94202899 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.92617450  0.92617450  0.30364293  0.98077697  0.96095008  0.60833221 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.71155373  0.81593915 94.20289855 
-#> 
-#> $ranger
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.04285714  0.95714286  0.90909091  0.97222222  0.97222222  0.94927536 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.93959732  0.93959732  0.15156344  0.98379630  0.96759259  0.74675595 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.83371476  0.81792328 94.20289855 
-#> 
-#> $xgboost
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.06190476  0.93809524  0.89333333  0.93055556  0.93055556  0.94202899 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.91156463  0.91156463  0.20105329  0.97247383  0.95249597  0.42783593 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.57727936  0.81048280 89.31159420
    -

    If you change the list object to tidy format, you’ll see the -following at a glance:

    +# Performance by analytics models +performance <- perf$performance +names(performance) <- perf$model_id +performance +#> $logistic +#> ZeroOneLoss Accuracy Precision Recall Sensitivity Specificity +#> 0.08571429 0.91428571 0.86486486 0.88888889 0.88888889 0.92753623 +#> F1_Score Fbeta_Score LogLoss AUC Gini PRAUC +#> 0.87671233 0.87671233 2.76028551 0.91339573 0.86714976 0.10267867 +#> LiftAUC GainAUC KS_Stat +#> 1.14456141 0.77166005 83.03140097 +#> +#> $rpart +#> ZeroOneLoss Accuracy Precision Recall Sensitivity Specificity +#> 0.08571429 0.91428571 0.85526316 0.90277778 0.90277778 0.92028986 +#> F1_Score Fbeta_Score LogLoss AUC Gini PRAUC +#> 0.87837838 0.87837838 0.76993613 0.92200081 0.86090982 0.76243464 +#> LiftAUC GainAUC KS_Stat +#> 1.91998633 0.77731481 83.63526570 +#> +#> $ctree +#> ZeroOneLoss Accuracy Precision Recall Sensitivity Specificity +#> 0.08571429 0.91428571 0.83750000 0.93055556 0.93055556 0.90579710 +#> F1_Score Fbeta_Score LogLoss AUC Gini PRAUC +#> 0.88157895 0.88157895 0.99432388 0.94509863 0.90438808 0.71643789 +#> LiftAUC GainAUC KS_Stat +#> 1.85328656 0.79249339 86.35265700 +#> +#> $randomForest +#> ZeroOneLoss Accuracy Precision Recall Sensitivity Specificity +#> 0.05238095 0.94761905 0.89610390 0.95833333 0.95833333 0.94202899 +#> F1_Score Fbeta_Score LogLoss AUC Gini PRAUC +#> 0.92617450 0.92617450 0.30364293 0.98077697 0.96095008 0.60833221 +#> LiftAUC GainAUC KS_Stat +#> 1.71155373 0.81593915 94.20289855 +#> +#> $ranger +#> ZeroOneLoss Accuracy Precision Recall Sensitivity Specificity +#> 0.04285714 0.95714286 0.90909091 0.97222222 0.97222222 0.94927536 +#> F1_Score Fbeta_Score LogLoss AUC Gini PRAUC +#> 0.93959732 0.93959732 0.15156344 0.98379630 0.96759259 0.74675595 +#> LiftAUC GainAUC KS_Stat +#> 1.83371476 0.81792328 94.20289855 +#> +#> $xgboost +#> ZeroOneLoss Accuracy Precision Recall Sensitivity Specificity +#> 0.06190476 0.93809524 0.89333333 0.93055556 0.93055556 0.94202899 +#> F1_Score Fbeta_Score LogLoss AUC Gini PRAUC +#> 0.91156463 0.91156463 0.20105329 0.97247383 0.95249597 0.42783593 +#> LiftAUC GainAUC KS_Stat +#> 1.57727936 0.81048280 89.31159420 +

    If you change the list object to tidy format, you’ll see the following at a glance:

    -# Convert to matrix for compare performace.
-sapply(performance, "c")
-#>                logistic       rpart       ctree randomForest      ranger
-#> ZeroOneLoss  0.08571429  0.08571429  0.08571429   0.05238095  0.04285714
-#> Accuracy     0.91428571  0.91428571  0.91428571   0.94761905  0.95714286
-#> Precision    0.86486486  0.85526316  0.83750000   0.89610390  0.90909091
-#> Recall       0.88888889  0.90277778  0.93055556   0.95833333  0.97222222
-#> Sensitivity  0.88888889  0.90277778  0.93055556   0.95833333  0.97222222
-#> Specificity  0.92753623  0.92028986  0.90579710   0.94202899  0.94927536
-#> F1_Score     0.87671233  0.87837838  0.88157895   0.92617450  0.93959732
-#> Fbeta_Score  0.87671233  0.87837838  0.88157895   0.92617450  0.93959732
-#> LogLoss      2.76028551  0.76993613  0.99432388   0.30364293  0.15156344
-#> AUC          0.91339573  0.92200081  0.94509863   0.98077697  0.98379630
-#> Gini         0.86714976  0.86090982  0.90438808   0.96095008  0.96759259
-#> PRAUC        0.10267867  0.76243464  0.71643789   0.60833221  0.74675595
-#> LiftAUC      1.14456141  1.91998633  1.85328656   1.71155373  1.83371476
-#> GainAUC      0.77166005  0.77731481  0.79249339   0.81593915  0.81792328
-#> KS_Stat     83.03140097 83.63526570 86.35265700  94.20289855 94.20289855
-#>                 xgboost
-#> ZeroOneLoss  0.06190476
-#> Accuracy     0.93809524
-#> Precision    0.89333333
-#> Recall       0.93055556
-#> Sensitivity  0.93055556
-#> Specificity  0.94202899
-#> F1_Score     0.91156463
-#> Fbeta_Score  0.91156463
-#> LogLoss      0.20105329
-#> AUC          0.97247383
-#> Gini         0.95249597
-#> PRAUC        0.42783593
-#> LiftAUC      1.57727936
-#> GainAUC      0.81048280
-#> KS_Stat     89.31159420
    -

    compare_performance() return a list object(results of -compared model performance). and list has the following components:

    +# Convert to matrix for compare performace. +sapply(performance, "c") +#> logistic rpart ctree randomForest ranger +#> ZeroOneLoss 0.08571429 0.08571429 0.08571429 0.05238095 0.04285714 +#> Accuracy 0.91428571 0.91428571 0.91428571 0.94761905 0.95714286 +#> Precision 0.86486486 0.85526316 0.83750000 0.89610390 0.90909091 +#> Recall 0.88888889 0.90277778 0.93055556 0.95833333 0.97222222 +#> Sensitivity 0.88888889 0.90277778 0.93055556 0.95833333 0.97222222 +#> Specificity 0.92753623 0.92028986 0.90579710 0.94202899 0.94927536 +#> F1_Score 0.87671233 0.87837838 0.88157895 0.92617450 0.93959732 +#> Fbeta_Score 0.87671233 0.87837838 0.88157895 0.92617450 0.93959732 +#> LogLoss 2.76028551 0.76993613 0.99432388 0.30364293 0.15156344 +#> AUC 0.91339573 0.92200081 0.94509863 0.98077697 0.98379630 +#> Gini 0.86714976 0.86090982 0.90438808 0.96095008 0.96759259 +#> PRAUC 0.10267867 0.76243464 0.71643789 0.60833221 0.74675595 +#> LiftAUC 1.14456141 1.91998633 1.85328656 1.71155373 1.83371476 +#> GainAUC 0.77166005 0.77731481 0.79249339 0.81593915 0.81792328 +#> KS_Stat 83.03140097 83.63526570 86.35265700 94.20289855 94.20289855 +#> xgboost +#> ZeroOneLoss 0.06190476 +#> Accuracy 0.93809524 +#> Precision 0.89333333 +#> Recall 0.93055556 +#> Sensitivity 0.93055556 +#> Specificity 0.94202899 +#> F1_Score 0.91156463 +#> Fbeta_Score 0.91156463 +#> LogLoss 0.20105329 +#> AUC 0.97247383 +#> Gini 0.95249597 +#> PRAUC 0.42783593 +#> LiftAUC 1.57727936 +#> GainAUC 0.81048280 +#> KS_Stat 89.31159420 +

    compare_performance() return a list object(results of compared model performance). and list has the following components:

      -
    • recommend_model : character. The name of the model that is -recommended as the best among the various models.
      • -
    • top_count : numeric. The number of best performing performance -metrics by model.
      • -
    • mean_rank : numeric. Average of ranking individual performance -metrics by model.
      • -
    • top_metric : list. The name of the performance metric with the best -performance on individual performance metrics by model.
      • +
    • recommend_model : character. The name of the model that is recommended as the best among the various models.
      • +
    • top_count : numeric. The number of best performing performance metrics by model.
      • +
    • mean_rank : numeric. Average of ranking individual performance metrics by model.
      • +
    • top_metric : list. The name of the performance metric with the best performance on individual performance metrics by model.
    -

    In this example, compare_performance() recommend the -“ranger” model.

    +

    In this example, compare_performance() recommend the “ranger” model.

    -# Compaire the Performance metrics of each model
-comp_perf <- compare_performance(pred)
-comp_perf
-#> $recommend_model
-#> [1] "ranger"
-#> 
-#> $top_metric_count
-#>     logistic        rpart        ctree randomForest       ranger      xgboost 
-#>            0            2            0            1           11            0 
-#> 
-#> $mean_rank
-#>     logistic        rpart        ctree randomForest       ranger      xgboost 
-#>     5.461538     4.384615     4.269231     2.384615     1.269231     3.230769 
-#> 
-#> $top_metric
-#> $top_metric$logistic
-#> NULL
-#> 
-#> $top_metric$rpart
-#> [1] "PRAUC"   "LiftAUC"
-#> 
-#> $top_metric$ctree
-#> NULL
-#> 
-#> $top_metric$randomForest
-#> [1] "KS_Stat"
-#> 
-#> $top_metric$ranger
-#>  [1] "ZeroOneLoss" "Accuracy"    "Precision"   "Recall"      "Specificity"
-#>  [6] "F1_Score"    "LogLoss"     "AUC"         "Gini"        "GainAUC"    
-#> [11] "KS_Stat"    
-#> 
-#> $top_metric$xgboost
-#> NULL
    +# Compaire the Performance metrics of each model +comp_perf <- compare_performance(pred) +comp_perf +#> $recommend_model +#> [1] "ranger" +#> +#> $top_metric_count +#> logistic rpart ctree randomForest ranger xgboost +#> 0 2 0 1 11 0 +#> +#> $mean_rank +#> logistic rpart ctree randomForest ranger xgboost +#> 5.461538 4.384615 4.269231 2.384615 1.269231 3.230769 +#> +#> $top_metric +#> $top_metric$logistic +#> NULL +#> +#> $top_metric$rpart +#> [1] "PRAUC" "LiftAUC" +#> +#> $top_metric$ctree +#> NULL +#> +#> $top_metric$randomForest +#> [1] "KS_Stat" +#> +#> $top_metric$ranger +#> [1] "ZeroOneLoss" "Accuracy" "Precision" "Recall" "Specificity" +#> [6] "F1_Score" "LogLoss" "AUC" "Gini" "GainAUC" +#> [11] "KS_Stat" +#> +#> $top_metric$xgboost +#> NULL

    Plot the ROC curve with plot_performance() @@ -1663,228 +1506,214 @@

    Plot the ROC curve with

    compare_performance() plot ROC curve.

    -# Plot ROC curve
-plot_performance(pred)
    +# Plot ROC curve +plot_performance(pred)

    Tunning the cut-off

    -

    In general, if the prediction probability is greater than 0.5 in the -binary classification model, it is predicted as -positive class. In other words, 0.5 is used for the cut-off -value. This applies to most model algorithms. However, in some cases, -the performance can be tuned by changing the cut-off value.

    -

    plot_cutoff () visualizes a plot to select the cut-off -value, and returns the cut-off value.

    +

    In general, if the prediction probability is greater than 0.5 in the binary classification model, it is predicted as positive class. In other words, 0.5 is used for the cut-off value. This applies to most model algorithms. However, in some cases, the performance can be tuned by changing the cut-off value.

    +

    plot_cutoff () visualizes a plot to select the cut-off value, and returns the cut-off value.

    -pred_best <- pred %>% 
-  filter(model_id == comp_perf$recommend_model) %>% 
-  select(predicted) %>% 
-  pull %>% 
-  .[[1]] %>% 
-  attr("pred_prob")
-
-cutoff <- plot_cutoff(pred_best, test$Class, "malignant", type = "mcc")
    +pred_best <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + select(predicted) %>% + pull %>% + .[[1]] %>% + attr("pred_prob") + +cutoff <- plot_cutoff(pred_best, test$Class, "malignant", type = "mcc")

    -cutoff
-#> [1] 0.36
-
-cutoff2 <- plot_cutoff(pred_best, test$Class, "malignant", type = "density")
    +cutoff +#> [1] 0.36 + +cutoff2 <- plot_cutoff(pred_best, test$Class, "malignant", type = "density") 

    -cutoff2
-#> [1] 0.8669
-
-cutoff3 <- plot_cutoff(pred_best, test$Class, "malignant", type = "prob")
    +cutoff2 +#> [1] 0.8669 + +cutoff3 <- plot_cutoff(pred_best, test$Class, "malignant", type = "prob") 

    -cutoff3
-#> [1] 0.36
    +cutoff3 +#> [1] 0.36
    -

    Performance comparison between prediction and tuned cut-off with -performance_metric() +

    Performance comparison between prediction and tuned cut-off with performance_metric()

    -

    Compare the performance of the original prediction with that of the -tuned cut-off. Compare the cut-off with the non-cut model for the model -with the best performance comp_perf$recommend_model.

    +

    Compare the performance of the original prediction with that of the tuned cut-off. Compare the cut-off with the non-cut model for the model with the best performance comp_perf$recommend_model.

    -comp_perf$recommend_model
-#> [1] "ranger"
-
-# extract predicted probability
-idx <- which(pred$model_id == comp_perf$recommend_model)
-pred_prob <- attr(pred$predicted[[idx]], "pred_prob")
-
-# or, extract predicted probability using dplyr
-pred_prob <- pred %>% 
-  filter(model_id == comp_perf$recommend_model) %>% 
-  select(predicted) %>% 
-  pull %>% 
-  "[["(1) %>% 
-  attr("pred_prob")
-
-# predicted probability
-pred_prob  
-#>   [1] 8.619087e-01 4.318254e-03 3.248235e-01 0.000000e+00 9.890222e-01
-#>   [6] 5.703044e-01 0.000000e+00 0.000000e+00 7.558754e-01 6.041921e-01
-#>  [11] 9.784675e-01 9.928103e-01 9.863167e-01 6.878302e-01 6.188595e-01
-#>  [16] 9.981000e-01 6.000000e-04 8.481627e-01 5.007690e-01 8.994254e-01
-#>  [21] 1.899524e-02 1.220667e-01 9.770968e-01 0.000000e+00 0.000000e+00
-#>  [26] 9.996000e-01 9.305024e-01 1.000000e-03 2.631698e-01 9.895690e-01
-#>  [31] 9.161913e-01 6.396825e-03 9.515587e-01 0.000000e+00 1.753000e-01
-#>  [36] 9.834079e-01 0.000000e+00 0.000000e+00 0.000000e+00 8.086889e-01
-#>  [41] 4.612595e-01 9.347826e-05 1.000000e+00 1.133260e-01 0.000000e+00
-#>  [46] 9.938452e-01 0.000000e+00 8.430500e-01 9.990000e-01 9.976667e-01
-#>  [51] 9.934905e-01 9.993333e-01 1.041349e-02 0.000000e+00 0.000000e+00
-#>  [56] 1.000000e+00 9.898603e-01 9.347826e-05 1.778951e-02 1.000000e+00
-#>  [61] 0.000000e+00 0.000000e+00 9.347826e-05 0.000000e+00 2.634778e-01
-#>  [66] 1.000000e+00 5.000000e-04 9.297103e-01 9.895294e-01 8.653397e-01
-#>  [71] 1.000000e+00 3.402460e-02 9.898278e-01 0.000000e+00 9.098643e-01
-#>  [76] 4.701206e-01 0.000000e+00 0.000000e+00 0.000000e+00 9.124984e-01
-#>  [81] 9.886984e-01 9.653143e-01 1.000000e+00 9.942127e-01 0.000000e+00
-#>  [86] 8.887706e-01 9.990000e-01 0.000000e+00 0.000000e+00 0.000000e+00
-#>  [91] 8.874833e-01 1.000000e+00 0.000000e+00 3.188889e-03 0.000000e+00
-#>  [96] 0.000000e+00 0.000000e+00 6.046397e-01 0.000000e+00 7.028357e-01
-#> [101] 8.783000e-01 0.000000e+00 1.000000e+00 1.000000e+00 9.347826e-05
-#> [106] 0.000000e+00 0.000000e+00 9.347826e-05 2.824214e-01 1.000000e+00
-#> [111] 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00
-#> [116] 9.868159e-01 0.000000e+00 0.000000e+00 0.000000e+00 9.347826e-05
-#> [121] 9.970667e-01 9.990000e-01 1.270381e-01 0.000000e+00 9.966952e-01
-#> [126] 5.000000e-04 9.973500e-01 9.800881e-01 0.000000e+00 9.781325e-01
-#> [131] 9.347826e-05 1.000000e+00 8.466014e-02 0.000000e+00 2.876190e-03
-#> [136] 0.000000e+00 9.988000e-01 9.993333e-01 1.285618e-02 0.000000e+00
-#> [141] 0.000000e+00 5.000000e-04 9.673270e-01 1.485618e-02 1.000000e+00
-#> [146] 0.000000e+00 7.958554e-02 8.228008e-01 3.602444e-01 0.000000e+00
-#> [151] 2.978951e-02 9.347826e-05 9.347826e-05 5.000000e-04 2.340476e-03
-#> [156] 0.000000e+00 0.000000e+00 8.836643e-01 9.347826e-05 9.347826e-05
-#> [161] 1.000000e+00 2.978951e-02 0.000000e+00 9.347826e-05 0.000000e+00
-#> [166] 2.498333e-02 0.000000e+00 0.000000e+00 0.000000e+00 1.401849e-01
-#> [171] 0.000000e+00 0.000000e+00 9.963373e-01 0.000000e+00 0.000000e+00
-#> [176] 0.000000e+00 9.773810e-01 0.000000e+00 0.000000e+00 9.347826e-05
-#> [181] 7.927944e-01 1.000000e+00 0.000000e+00 0.000000e+00 2.709524e-03
-#> [186] 9.347826e-05 8.845437e-01 1.098856e-02 0.000000e+00 4.488889e-03
-#> [191] 0.000000e+00 0.000000e+00 9.629365e-03 0.000000e+00 0.000000e+00
-#> [196] 0.000000e+00 5.000000e-04 0.000000e+00 0.000000e+00 0.000000e+00
-#> [201] 0.000000e+00 9.984127e-01 9.728571e-03 0.000000e+00 0.000000e+00
-#> [206] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 9.812944e-01
-
-# compaire Accuracy
-performance_metric(pred_prob, test$Class, "malignant", "Accuracy")
-#> [1] 0.9571429
-performance_metric(pred_prob, test$Class, "malignant", "Accuracy",
-                   cutoff = cutoff)
-#> [1] 0.9619048
-
-# compaire Confusion Matrix
-performance_metric(pred_prob, test$Class, "malignant", "ConfusionMatrix")
-#>            actual
-#> predict     benign malignant
-#>   benign       131         2
-#>   malignant      7        70
-performance_metric(pred_prob, test$Class, "malignant", "ConfusionMatrix", 
-                   cutoff = cutoff)
-#>            actual
-#> predict     benign malignant
-#>   benign       130         0
-#>   malignant      8        72
-
-# compaire F1 Score
-performance_metric(pred_prob, test$Class, "malignant", "F1_Score")
-#> [1] 0.9395973
-performance_metric(pred_prob, test$Class,  "malignant", "F1_Score", 
-                   cutoff = cutoff)
-#> [1] 0.9473684
-performance_metric(pred_prob, test$Class,  "malignant", "F1_Score", 
-                   cutoff = cutoff2)
-#> [1] 0.880597
    -

    If the performance of the tuned cut-off is good, use it as a cut-off -to predict positives.

    +comp_perf$recommend_model +#> [1] "ranger" + +# extract predicted probability +idx <- which(pred$model_id == comp_perf$recommend_model) +pred_prob <- attr(pred$predicted[[idx]], "pred_prob") + +# or, extract predicted probability using dplyr +pred_prob <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + select(predicted) %>% + pull %>% + "[["(1) %>% + attr("pred_prob") + +# predicted probability +pred_prob +#> [1] 8.619087e-01 4.318254e-03 3.248235e-01 0.000000e+00 9.890222e-01 +#> [6] 5.703044e-01 0.000000e+00 0.000000e+00 7.558754e-01 6.041921e-01 +#> [11] 9.784675e-01 9.928103e-01 9.863167e-01 6.878302e-01 6.188595e-01 +#> [16] 9.981000e-01 6.000000e-04 8.481627e-01 5.007690e-01 8.994254e-01 +#> [21] 1.899524e-02 1.220667e-01 9.770968e-01 0.000000e+00 0.000000e+00 +#> [26] 9.996000e-01 9.305024e-01 1.000000e-03 2.631698e-01 9.895690e-01 +#> [31] 9.161913e-01 6.396825e-03 9.515587e-01 0.000000e+00 1.753000e-01 +#> [36] 9.834079e-01 0.000000e+00 0.000000e+00 0.000000e+00 8.086889e-01 +#> [41] 4.612595e-01 9.347826e-05 1.000000e+00 1.133260e-01 0.000000e+00 +#> [46] 9.938452e-01 0.000000e+00 8.430500e-01 9.990000e-01 9.976667e-01 +#> [51] 9.934905e-01 9.993333e-01 1.041349e-02 0.000000e+00 0.000000e+00 +#> [56] 1.000000e+00 9.898603e-01 9.347826e-05 1.778951e-02 1.000000e+00 +#> [61] 0.000000e+00 0.000000e+00 9.347826e-05 0.000000e+00 2.634778e-01 +#> [66] 1.000000e+00 5.000000e-04 9.297103e-01 9.895294e-01 8.653397e-01 +#> [71] 1.000000e+00 3.402460e-02 9.898278e-01 0.000000e+00 9.098643e-01 +#> [76] 4.701206e-01 0.000000e+00 0.000000e+00 0.000000e+00 9.124984e-01 +#> [81] 9.886984e-01 9.653143e-01 1.000000e+00 9.942127e-01 0.000000e+00 +#> [86] 8.887706e-01 9.990000e-01 0.000000e+00 0.000000e+00 0.000000e+00 +#> [91] 8.874833e-01 1.000000e+00 0.000000e+00 3.188889e-03 0.000000e+00 +#> [96] 0.000000e+00 0.000000e+00 6.046397e-01 0.000000e+00 7.028357e-01 +#> [101] 8.783000e-01 0.000000e+00 1.000000e+00 1.000000e+00 9.347826e-05 +#> [106] 0.000000e+00 0.000000e+00 9.347826e-05 2.824214e-01 1.000000e+00 +#> [111] 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 +#> [116] 9.868159e-01 0.000000e+00 0.000000e+00 0.000000e+00 9.347826e-05 +#> [121] 9.970667e-01 9.990000e-01 1.270381e-01 0.000000e+00 9.966952e-01 +#> [126] 5.000000e-04 9.973500e-01 9.800881e-01 0.000000e+00 9.781325e-01 +#> [131] 9.347826e-05 1.000000e+00 8.466014e-02 0.000000e+00 2.876190e-03 +#> [136] 0.000000e+00 9.988000e-01 9.993333e-01 1.285618e-02 0.000000e+00 +#> [141] 0.000000e+00 5.000000e-04 9.673270e-01 1.485618e-02 1.000000e+00 +#> [146] 0.000000e+00 7.958554e-02 8.228008e-01 3.602444e-01 0.000000e+00 +#> [151] 2.978951e-02 9.347826e-05 9.347826e-05 5.000000e-04 2.340476e-03 +#> [156] 0.000000e+00 0.000000e+00 8.836643e-01 9.347826e-05 9.347826e-05 +#> [161] 1.000000e+00 2.978951e-02 0.000000e+00 9.347826e-05 0.000000e+00 +#> [166] 2.498333e-02 0.000000e+00 0.000000e+00 0.000000e+00 1.401849e-01 +#> [171] 0.000000e+00 0.000000e+00 9.963373e-01 0.000000e+00 0.000000e+00 +#> [176] 0.000000e+00 9.773810e-01 0.000000e+00 0.000000e+00 9.347826e-05 +#> [181] 7.927944e-01 1.000000e+00 0.000000e+00 0.000000e+00 2.709524e-03 +#> [186] 9.347826e-05 8.845437e-01 1.098856e-02 0.000000e+00 4.488889e-03 +#> [191] 0.000000e+00 0.000000e+00 9.629365e-03 0.000000e+00 0.000000e+00 +#> [196] 0.000000e+00 5.000000e-04 0.000000e+00 0.000000e+00 0.000000e+00 +#> [201] 0.000000e+00 9.984127e-01 9.728571e-03 0.000000e+00 0.000000e+00 +#> [206] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 9.812944e-01 + +# compaire Accuracy +performance_metric(pred_prob, test$Class, "malignant", "Accuracy") +#> [1] 0.9571429 +performance_metric(pred_prob, test$Class, "malignant", "Accuracy", + cutoff = cutoff) +#> [1] 0.9619048 + +# compaire Confusion Matrix +performance_metric(pred_prob, test$Class, "malignant", "ConfusionMatrix") +#> actual +#> predict benign malignant +#> benign 131 2 +#> malignant 7 70 +performance_metric(pred_prob, test$Class, "malignant", "ConfusionMatrix", + cutoff = cutoff) +#> actual +#> predict benign malignant +#> benign 130 0 +#> malignant 8 72 + +# compaire F1 Score +performance_metric(pred_prob, test$Class, "malignant", "F1_Score") +#> [1] 0.9395973 +performance_metric(pred_prob, test$Class, "malignant", "F1_Score", + cutoff = cutoff) +#> [1] 0.9473684 +performance_metric(pred_prob, test$Class, "malignant", "F1_Score", + cutoff = cutoff2) +#> [1] 0.880597
    +

    If the performance of the tuned cut-off is good, use it as a cut-off to predict positives.

    Predict

    -

    If you have selected a good model from several models, then perform -the prediction with that model.

    +

    If you have selected a good model from several models, then perform the prediction with that model.

    Create data set for predict

    -

    Create sample data for predicting by extracting 100 samples from the -data set used in the previous under sampling example.

    +

    Create sample data for predicting by extracting 100 samples from the data set used in the previous under sampling example.

    -data_pred <- train_under %>% 
-  cleanse 
-#> ── Checking unique value ─────────────────────────── unique value is one ──
-#> No variables that unique value is one.
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
-#> remove variables with high unique rate
-#> ● Id = 325(0.961538461538462)
-#> 
-#> ── Checking character variables ─────────────────────── categorical data ──
-#> No character variables.
-
-set.seed(1234L)
-data_pred <- data_pred %>% 
-  nrow %>% 
-  seq %>% 
-  sample(size = 50) %>% 
-  data_pred[., ]
    +data_pred <- train_under %>% + cleanse +#> ── Checking unique value ─────────────────────────── unique value is one ── +#> No variables that unique value is one. +#> +#> ── Checking unique rate ─────────────────────────────── high unique rate ── +#> remove variables with high unique rate +#> ● Id = 325(0.961538461538462) +#> +#> ── Checking character variables ─────────────────────── categorical data ── +#> No character variables. + +set.seed(1234L) +data_pred <- data_pred %>% + nrow %>% + seq %>% + sample(size = 50) %>% + data_pred[., ]

    Predict with alookr and dplyr

    -

    Do a predict using the dplyr package. The last -factor() function eliminates unnecessary information.

    +

    Do a predict using the dplyr package. The last factor() function eliminates unnecessary information.

    -pred_actual <- pred %>%
-  filter(model_id == comp_perf$recommend_model) %>% 
-  run_predict(data_pred) %>% 
-  select(predicted) %>% 
-  pull %>% 
-  "[["(1) %>% 
-  factor()
-
-pred_actual
-#>  [1] malignant malignant benign    malignant malignant malignant benign   
-#>  [8] benign    benign    benign    malignant malignant malignant benign   
-#> [15] malignant benign    benign    malignant malignant malignant malignant
-#> [22] benign    malignant malignant benign    benign    malignant malignant
-#> [29] benign    malignant malignant malignant malignant benign    benign   
-#> [36] benign    malignant benign    malignant malignant benign    malignant
-#> [43] malignant malignant benign    benign    benign    malignant malignant
-#> [50] benign   
-#> Levels: benign malignant
    -

    If you want to predict by cut-off, specify the cutoff -argument in the run_predict() function as follows.:

    -

    In the example, there is no difference between the results of using -cut-off and not.

    +pred_actual <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + run_predict(data_pred) %>% + select(predicted) %>% + pull %>% + "[["(1) %>% + factor() + +pred_actual +#> [1] malignant malignant benign malignant malignant malignant benign +#> [8] benign benign benign malignant malignant malignant benign +#> [15] malignant benign benign malignant malignant malignant malignant +#> [22] benign malignant malignant benign benign malignant malignant +#> [29] benign malignant malignant malignant malignant benign benign +#> [36] benign malignant benign malignant malignant benign malignant +#> [43] malignant malignant benign benign benign malignant malignant +#> [50] benign +#> Levels: benign malignant
    +

    If you want to predict by cut-off, specify the cutoff argument in the run_predict() function as follows.:

    +

    In the example, there is no difference between the results of using cut-off and not.

    -pred_actual2 <- pred %>%
-  filter(model_id == comp_perf$recommend_model) %>% 
-  run_predict(data_pred, cutoff) %>% 
-  select(predicted) %>% 
-  pull %>% 
-  "[["(1) %>% 
-  factor()
-
-pred_actual2
-#>  [1] malignant malignant benign    malignant malignant malignant benign   
-#>  [8] benign    benign    benign    malignant malignant malignant benign   
-#> [15] malignant benign    benign    malignant malignant malignant malignant
-#> [22] benign    malignant malignant benign    benign    malignant malignant
-#> [29] benign    malignant malignant malignant malignant benign    benign   
-#> [36] benign    malignant benign    malignant malignant benign    malignant
-#> [43] malignant malignant benign    benign    benign    malignant malignant
-#> [50] benign   
-#> Levels: benign malignant
-
-sum(pred_actual != pred_actual2)
-#> [1] 0
    +pred_actual2 <- pred %>% + filter(model_id == comp_perf$recommend_model) %>% + run_predict(data_pred, cutoff) %>% + select(predicted) %>% + pull %>% + "[["(1) %>% + factor() + +pred_actual2 +#> [1] malignant malignant benign malignant malignant malignant benign +#> [8] benign benign benign malignant malignant malignant benign +#> [15] malignant benign benign malignant malignant malignant malignant +#> [22] benign malignant malignant benign benign malignant malignant +#> [29] benign malignant malignant malignant malignant benign benign +#> [36] benign malignant benign malignant malignant benign malignant +#> [43] malignant malignant benign benign benign malignant malignant +#> [50] benign +#> Levels: benign malignant + +sum(pred_actual != pred_actual2) +#> [1] 0 @@ -1904,9 +1733,7 @@

    Links

    License

    @@ -1944,8 +1771,7 @@

    Dev status

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

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    +
    + + + +
    +
    +
    +

    Changelog

    + Source: NEWS.md +
    + +
    +

    alookr 0.3.92024-02-11

    +
    +

    MINOR CHANGES

    +
    • Fix error in treatment_corr() that is “All columns in a tibble must be vectors.” error. +
      • (#6, thanks to Cathy Tomson)
        • +
      • +
    +
    +
    +

    alookr 0.3.8

    +
    +

    BUG FIXES

    +
    • Fix error in treatment_corr() that is “All columns in a tibble must be vectors.” error. +
      • (#6, thanks to Cathy Tomson)
        • +
      • +
    +
    +
    +

    alookr 0.3.72022-06-12

    +
    +

    MAJOR CHANGES

    +
    • Removed plan(multiprocess) from logic for parallel processing. +
      • Because, plan(multiprocess) of future is deprecated. (#2, thanks to Henrik Bengtsson)
        • +
      • +
    +
    +

    MINOR CHANGES

    +
    • Remove the warning of “UNRELIABLE VALUE” with seed = TRUE in future function.
      • +
    +
    +

    BUG FIXES

    +
    • Fix error in run_performance() that is “replacement has length zero” error. +
      • (#5, thanks to Muhammad Fawad)
        • +
      • +
    +
    +
    +

    alookr 0.3.62022-05-25

    +
    +

    MINOR CHANGES

    +
    • Implemented a function to replace the unbalanced package used in the process of performing split data. +
      • This is because unbalanced packages have been removed from CRAN. (#3)
        • +
      • +
    +
    +
    +

    alookr 0.3.52021-12-01

    +
    +

    BUG FIXES

    +
    • Fix error in glmnet when run_predict() is performed with test data that has more variables than train data.
      • +
    +
    +
    +

    alookr 0.3.42021-02-22

    +
    +

    MAJOR CHANGES

    +
    • add xgboosting methodlogy for binary classifier.
      • +
    • add lasso regression model for binary classifier.
      • +
    +
    +
    +

    alookr 0.3.3

    +
    +

    BUG FIXES

    +
    +
    +

    MINOR CHANGES

    +
    +
    +
    +

    alookr 0.3.22020-06-07

    +
    +

    BUG FIXES

    +
    • Fixed explanation errors in Classification Modeling vignettes for debian linux.
      • +
    +
    +

    MINOR CHANGES

    +
    +
    +
    +

    alookr 0.3.1

    +
    +

    BUG FIXES

    +
    • Fixed explanation errors in Cleansing the dataset vignettes.
      • +
    • Fixed explanation errors in Classification Modeling vignettes.
      • +
    • Modified explanation errors in Splitting the dataset vignettes.
      • +
    +
    +
    + + + +
    + + +
    +

    Developed by Choonghyun Ryu.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/docs/pkgdown.css b/docs/pkgdown.css index c01e592..80ea5b8 100644 --- a/docs/pkgdown.css +++ b/docs/pkgdown.css @@ -56,8 +56,10 @@ img.icon { float: right; } -img { +/* Ensure in-page images don't run outside their container */ +.contents img { max-width: 100%; + height: auto; } /* Fix bug in bootstrap (only seen in firefox) */ @@ -78,11 +80,10 @@ dd { /* Section anchors ---------------------------------*/ a.anchor { - margin-left: -30px; - display:inline-block; - width: 30px; - height: 30px; - visibility: hidden; + display: none; + margin-left: 5px; + width: 20px; + height: 20px; background-image: url(./link.svg); background-repeat: no-repeat; @@ -90,17 +91,15 @@ a.anchor { background-position: center center; } -.hasAnchor:hover a.anchor { - visibility: visible; -} - -@media (max-width: 767px) { - .hasAnchor:hover a.anchor { - visibility: hidden; - } +h1:hover .anchor, +h2:hover .anchor, +h3:hover .anchor, +h4:hover .anchor, +h5:hover .anchor, +h6:hover .anchor { + display: inline-block; } - /* Fixes for fixed navbar --------------------------*/ .contents h1, .contents h2, .contents h3, .contents h4 { @@ -244,14 +243,14 @@ nav[data-toggle='toc'] .nav .nav > .active:focus > a { .ref-index th {font-weight: normal;} -.ref-index td {vertical-align: top;} +.ref-index td {vertical-align: top; min-width: 100px} .ref-index .icon {width: 40px;} .ref-index .alias {width: 40%;} .ref-index-icons .alias {width: calc(40% - 40px);} .ref-index .title {width: 60%;} .ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} .ref-arguments .name {width: 20%;} .ref-arguments .desc {width: 80%;} @@ -264,31 +263,26 @@ table { /* Syntax highlighting ---------------------------------------------------- */ -pre { - word-wrap: normal; - word-break: normal; - border: 1px solid #eee; -} - -pre, code { +pre, code, pre code { background-color: #f8f8f8; color: #333; } +pre, pre code { + white-space: pre-wrap; + word-break: break-all; + overflow-wrap: break-word; +} -pre code { - overflow: auto; - word-wrap: normal; - white-space: pre; +pre { + border: 1px solid #eee; } -pre .img { +pre .img, pre .r-plt { margin: 5px 0; } -pre .img img { +pre .img img, pre .r-plt img { background-color: #fff; - display: block; - height: auto; } code a, pre a { @@ -305,9 +299,8 @@ a.sourceLine:hover { .kw {color: #264D66;} /* keyword */ .co {color: #888888;} /* comment */ -.message { color: black; font-weight: bolder;} -.error { color: orange; font-weight: bolder;} -.warning { color: #6A0366; font-weight: bolder;} +.error {font-weight: bolder;} +.warning {font-weight: bolder;} /* Clipboard --------------------------*/ @@ -365,3 +358,27 @@ mark { content: ""; } } + +/* Section anchors --------------------------------- + Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 +*/ + +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 7e7048f..6f0eee4 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -80,7 +80,7 @@ $(document).ready(function() { var copyButton = ""; - $(".examples, div.sourceCode").addClass("hasCopyButton"); + $("div.sourceCode").addClass("hasCopyButton"); // Insert copy buttons: $(copyButton).prependTo(".hasCopyButton"); @@ -91,7 +91,7 @@ // Initialize clipboard: var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { text: function(trigger) { - return trigger.parentNode.textContent; + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); } }); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 09be140..682d4bb 100644 --- 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    -
    - -
    - -
    +

    Cleansing the dataset for classification modeling

    - Source: R/preprocess.R, R/split.R + Source: R/preprocess.R, R/split.R
    @@ -120,161 +58,198 @@

    Cleansing the dataset for classification modeling

    The cleanse() cleanse the dataset for classification modeling

    - 
    # S3 method for data.frame
-cleanse(
-  .data,
-  uniq = TRUE,
-  uniq_thres = 0.1,
-  char = TRUE,
-  missing = FALSE,
-  verbose = TRUE,
-  ...
-)
+    
    
+    
    # S3 method for data.frame
+cleanse(
+  .data,
+  uniq = TRUE,
+  uniq_thres = 0.1,
+  char = TRUE,
+  missing = FALSE,
+  verbose = TRUE,
+  ...
+)
+
+cleanse(.data, ...)
    
+    
    
 
-cleanse(.data, ...)
    +
    +

    Arguments

    +
    .data
    +

    a data.frame or a tbl_df.

    -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    .data

    a data.frame or a tbl_df.

    uniq

    logical. Set whether to remove the variables whose unique value is one.

    uniq_thres

    numeric. Set a threshold to removing variables when the ratio of unique values(number of unique values / number of observation) is greater than the set value.

    char

    logical. Set the change the character to factor.

    missing

    logical. Set whether to removing variables including missing value

    verbose

    logical. Set whether to echo information to the console at runtime.

    ...

    further arguments passed to or from other methods.

    -

    Value

    +
    uniq
    +

    logical. Set whether to remove the variables whose unique value is one.

    -

    An object of data.frame or train_df. and return value is an object of the same type as the .data argument.

    -

    Details

    -

    This function is useful when fit the classification model. -This function does the following.: -Remove the variable with only one value. And remove variables that have a unique number of values relative to the number of observations for a character or categorical variable. In this case, it is a variable that corresponds to an identifier or an identifier. And converts the character to factor.

    +
    uniq_thres
    +

    numeric. Set a threshold to removing variables when the ratio of unique values(number of unique values / number of observation) is greater than the set value.

    + + +
    char
    +

    logical. Set the change the character to factor.

    + -

    Examples

    - 
    # create sample dataset
-set.seed(123L)
-id <- sapply(1:1000, function(x)
-  paste(c(sample(letters, 5), x), collapse = ""))
+
    missing
    
+
    logical. Set whether to removing variables including missing value
    
 
-year <- "2018"
 
-set.seed(123L)
-count <- sample(1:10, size = 1000, replace = TRUE)
+
    verbose
    
+
    logical. Set whether to echo information to the console at runtime.
    
 
-set.seed(123L)
-alpha <- sample(letters, size = 1000, replace = TRUE)
 
-set.seed(123L)
-flag <- sample(c("Y", "N"), size = 1000, prob = c(0.1, 0.9), replace = TRUE)
+
    ...
    
+
    further arguments passed to or from other methods.
    
 
-dat <- data.frame(id, year, count, alpha, flag, stringsAsFactors = FALSE)
-# structure of dataset
-str(dat)
    #> 'data.frame':	1000 obs. of  5 variables:
-#>  $ id   : chr  "osncj1" "rvket2" "nvesi3" "chgji4" ...
-#>  $ year : chr  "2018" "2018" "2018" "2018" ...
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: chr  "o" "s" "n" "c" ...
-#>  $ flag : chr  "N" "N" "N" "N" ...
    
-# cleansing dataset
-newDat <- cleanse(dat)
    #> ── Checking unique value ─────────────────────────── unique value is one ──
    #> remove variables that unique value is one
    #> ● year
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
    #> remove variables with high unique rate
    #> ● id = 1000(1)
-#> 
-#> ── Checking character variables ─────────────────────── categorical data ──
    #> converts character variables to factor
    #> ● alpha
-#> ● flag
-#> 
    
-# structure of cleansing dataset
-str(newDat)
    #> 'data.frame':	1000 obs. of  3 variables:
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
-#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
    
-# cleansing dataset
-newDat <- cleanse(dat, uniq = FALSE)
    #> ── Checking character variables ─────────────────────── categorical data ──
    #> converts character variables to factor
    #> ● id
-#> ● year
-#> ● alpha
-#> ● flag
-#> 
    
-# structure of cleansing dataset
-str(newDat)
    #> 'data.frame':	1000 obs. of  5 variables:
-#>  $ id   : Factor w/ 1000 levels "ablnc282","abqym54",..: 594 715 558 94 727 270 499 882 930 515 ...
-#>  $ year : Factor w/ 1 level "2018": 1 1 1 1 1 1 1 1 1 1 ...
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
-#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
    
-# cleansing dataset
-newDat <- cleanse(dat, uniq_thres = 0.3)
    #> ── Checking unique value ─────────────────────────── unique value is one ──
    #> remove variables that unique value is one
    #> ● year
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
    #> remove variables with high unique rate
    #> ● id = 1000(1)
-#> 
-#> ── Checking character variables ─────────────────────── categorical data ──
    #> converts character variables to factor
    #> ● alpha
-#> ● flag
-#> 
    
-# structure of cleansing dataset
-str(newDat)
    #> 'data.frame':	1000 obs. of  3 variables:
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
-#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
    
-# cleansing dataset
-newDat <- cleanse(dat, char = FALSE)
    #> ── Checking unique value ─────────────────────────── unique value is one ──
    #> remove variables that unique value is one
    #> ● year
-#> 
-#> ── Checking unique rate ─────────────────────────────── high unique rate ──
    #> remove variables with high unique rate
    #> ● id = 1000(1)
-#> 
    
-# structure of cleansing dataset
-str(newDat)
    #> 'data.frame':	1000 obs. of  3 variables:
-#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
-#>  $ alpha: chr  "o" "s" "n" "c" ...
-#>  $ flag : chr  "N" "N" "N" "N" ...
    
-
    +
    +
    +

    Value

    + + +

    An object of data.frame or train_df. and return value is an object of the same type as the .data argument.

    +
    +
    +

    Details

    +

    This function is useful when fit the classification model. +This function does the following.: +Remove the variable with only one value. And remove variables that have a unique number of values relative to the number of observations for a character or categorical variable. In this case, it is a variable that corresponds to an identifier or an identifier. And converts the character to factor.

    +
    + +
    +

    Examples

    + 
    # create sample dataset
+set.seed(123L)
+id <- sapply(1:1000, function(x)
+  paste(c(sample(letters, 5), x), collapse = ""))
+
+year <- "2018"
+
+set.seed(123L)
+count <- sample(1:10, size = 1000, replace = TRUE)
+
+set.seed(123L)
+alpha <- sample(letters, size = 1000, replace = TRUE)
+
+set.seed(123L)
+flag <- sample(c("Y", "N"), size = 1000, prob = c(0.1, 0.9), replace = TRUE)
+
+dat <- data.frame(id, year, count, alpha, flag, stringsAsFactors = FALSE)
+# structure of dataset
+str(dat)
+#> 'data.frame':	1000 obs. of  5 variables:
+#>  $ id   : chr  "osncj1" "rvket2" "nvesi3" "chgji4" ...
+#>  $ year : chr  "2018" "2018" "2018" "2018" ...
+#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
+#>  $ alpha: chr  "o" "s" "n" "c" ...
+#>  $ flag : chr  "N" "N" "N" "N" ...
+
+# cleansing dataset
+newDat <- cleanse(dat)
+#> ── Checking unique value ─────────────────────────── unique value is one ──
+#> remove variables that unique value is one
+#> • year
+#> 
+#> ── Checking unique rate ─────────────────────────────── high unique rate ──
+#> remove variables with high unique rate
+#> • id = 1000(1)
+#> 
+#> ── Checking character variables ─────────────────────── categorical data ──
+#> converts character variables to factor
+#> • alpha
+#> • flag
+#> 
+
+# structure of cleansing dataset
+str(newDat)
+#> 'data.frame':	1000 obs. of  3 variables:
+#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
+#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
+#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
+
+# cleansing dataset
+newDat <- cleanse(dat, uniq = FALSE)
+#> ── Checking character variables ─────────────────────── categorical data ──
+#> converts character variables to factor
+#> • id
+#> • year
+#> • alpha
+#> • flag
+#> 
+
+# structure of cleansing dataset
+str(newDat)
+#> 'data.frame':	1000 obs. of  5 variables:
+#>  $ id   : Factor w/ 1000 levels "ablnc282","abqym54",..: 594 715 558 94 727 270 499 882 930 515 ...
+#>  $ year : Factor w/ 1 level "2018": 1 1 1 1 1 1 1 1 1 1 ...
+#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
+#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
+#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
+
+# cleansing dataset
+newDat <- cleanse(dat, uniq_thres = 0.3)
+#> ── Checking unique value ─────────────────────────── unique value is one ──
+#> remove variables that unique value is one
+#> • year
+#> 
+#> ── Checking unique rate ─────────────────────────────── high unique rate ──
+#> remove variables with high unique rate
+#> • id = 1000(1)
+#> 
+#> ── Checking character variables ─────────────────────── categorical data ──
+#> converts character variables to factor
+#> • alpha
+#> • flag
+#> 
+
+# structure of cleansing dataset
+str(newDat)
+#> 'data.frame':	1000 obs. of  3 variables:
+#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
+#>  $ alpha: Factor w/ 26 levels "a","b","c","d",..: 15 19 14 3 10 18 22 11 5 20 ...
+#>  $ flag : Factor w/ 2 levels "N","Y": 1 1 1 1 2 1 1 1 1 1 ...
+
+# cleansing dataset
+newDat <- cleanse(dat, char = FALSE)
+#> ── Checking unique value ─────────────────────────── unique value is one ──
+#> remove variables that unique value is one
+#> • year
+#> 
+#> ── Checking unique rate ─────────────────────────────── high unique rate ──
+#> remove variables with high unique rate
+#> • id = 1000(1)
+#> 
+
+# structure of cleansing dataset
+str(newDat)
+#> 'data.frame':	1000 obs. of  3 variables:
+#>  $ count: int  3 3 10 2 6 5 4 6 9 10 ...
+#>  $ alpha: chr  "o" "s" "n" "c" ...
+#>  $ flag : chr  "N" "N" "N" "N" ...
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/cleanse.split_df.html b/docs/reference/cleanse.split_df.html index 306f914..348a3be 100644 --- a/docs/reference/cleanse.split_df.html +++ b/docs/reference/cleanse.split_df.html @@ -1,67 +1,12 @@ - - - - - - - -Cleansing the dataset for classification modeling — cleanse.split_df • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Cleansing the dataset for classification modeling — cleanse.split_df • alookr - + + - - - -
    -
    - -
    - -
    +

    Cleansing the dataset for classification modeling

    - Source: R/split.R + Source: R/split.R
    @@ -120,105 +58,116 @@

    Cleansing the dataset for classification modeling

    Diagnosis of similarity between datasets splitted by train set and set included in the "split_df" class. and cleansing the "split_df" class

    - 
    # S3 method for split_df
-cleanse(.data, add_character = FALSE, uniq_thres = 0.9, missing = FALSE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    .data

    an object of class "split_df", usually, a result of a call to split_df().

    add_character

    logical. Decide whether to include text variables in the -compare of categorical data. The default value is FALSE, which also not includes character variables.

    uniq_thres

    numeric. Set a threshold to removing variables when the ratio of unique values(number of unique values / number of observation) is greater than the set value.

    missing

    logical. Set whether to removing variables including missing value

    ...

    further arguments passed to or from other methods.

    - -

    Value

    - -

    An object of class "split_df".

    -

    Details

    +
    + 
    # S3 method for split_df
+cleanse(.data, add_character = FALSE, uniq_thres = 0.9, missing = FALSE, ...)
    +
    + +
    +

    Arguments

    +
    .data
    +

    an object of class "split_df", usually, a result of a call to split_df().

    + + +
    add_character
    +

    logical. Decide whether to include text variables in the +compare of categorical data. The default value is FALSE, which also not includes character variables.

    + + +
    uniq_thres
    +

    numeric. Set a threshold to removing variables when the ratio of unique values(number of unique values / number of observation) is greater than the set value.

    + +
    missing
    +

    logical. Set whether to removing variables including missing value

    + + +
    ...
    +

    further arguments passed to or from other methods.

    + +
    +
    +

    Value

    + + +

    An object of class "split_df".

    +
    +
    +

    Details

    Remove the detected variables from the diagnosis using the compare_diag() function.

    +
    -

    Examples

    - 
    library(dplyr)
    #> 
-#> Attaching package: ‘dplyr’
    #> The following object is masked from ‘package:randomForest’:
-#> 
-#>     combine
    #> The following objects are masked from ‘package:stats’:
-#> 
-#>     filter, lag
    #> The following objects are masked from ‘package:base’:
-#> 
-#>     intersect, setdiff, setequal, union
    
-# Credit Card Default Data
-head(ISLR::Default)
    #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
    
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-sb %>%
-  cleanse
    #> There were no diagnostics issues
    #> # A tibble: 10,000 x 5
-#> # Groups:   split_flag [2]
-#>    default student balance income split_flag
-#>    <fct>   <fct>     <dbl>  <dbl> <chr>     
-#>  1 No      No         730. 44362. train     
-#>  2 No      Yes        817. 12106. train     
-#>  3 No      No        1074. 31767. train     
-#>  4 No      No         529. 35704. train     
-#>  5 No      No         786. 38463. train     
-#>  6 No      Yes        920.  7492. train     
-#>  7 No      No         826. 24905. test      
-#>  8 No      Yes        809. 17600. test      
-#>  9 No      No        1161. 37469. train     
-#> 10 No      No           0  29275. train     
-#> # … with 9,990 more rows
    
-
    +
    +

    Examples

    + 
    library(dplyr)
+#> 
+#> Attaching package: ‘dplyr’
+#> The following object is masked from ‘package:randomForest’:
+#> 
+#>     combine
+#> The following objects are masked from ‘package:stats’:
+#> 
+#>     filter, lag
+#> The following objects are masked from ‘package:base’:
+#> 
+#>     intersect, setdiff, setequal, union
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+sb %>%
+  cleanse
+#> There were no diagnostics issues
+#> # A tibble: 10,000 × 5
+#> # Groups:   split_flag [2]
+#>    default student balance income split_flag
+#>    <fct>   <fct>     <dbl>  <dbl> <chr>     
+#>  1 No      No         730. 44362. train     
+#>  2 No      Yes        817. 12106. train     
+#>  3 No      No        1074. 31767. train     
+#>  4 No      No         529. 35704. train     
+#>  5 No      No         786. 38463. train     
+#>  6 No      Yes        920.  7492. train     
+#>  7 No      No         826. 24905. test      
+#>  8 No      Yes        809. 17600. test      
+#>  9 No      No        1161. 37469. train     
+#> 10 No      No           0  29275. train     
+#> # ℹ 9,990 more rows
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/compare_diag.html b/docs/reference/compare_diag.html index 7ef15dd..af6bd20 100644 --- a/docs/reference/compare_diag.html +++ b/docs/reference/compare_diag.html @@ -1,67 +1,12 @@ - - - - - - - -Diagnosis of train set and test set of split_df object — compare_diag • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Diagnosis of train set and test set of split_df object — compare_diag • alookr - - + + - - -
    -
    - -
    - -
    +

    Diagnosis of train set and test set of split_df object

    - Source: R/split.R + Source: R/split.R
    @@ -120,201 +58,215 @@

    Diagnosis of train set and test set of split_df object

    Diagnosis of similarity between datasets splitted by train set and set included in the "split_df" class.

    - 
    compare_diag(
-  .data,
-  add_character = FALSE,
-  uniq_thres = 0.01,
-  miss_msg = TRUE,
-  verbose = TRUE
-)
    +
    + 
    compare_diag(
+  .data,
+  add_character = FALSE,
+  uniq_thres = 0.01,
+  miss_msg = TRUE,
+  verbose = TRUE
+)
    +
    + +
    +

    Arguments

    +
    .data
    +

    an object of class "split_df", usually, a result of a call to split_df().

    + + +
    add_character
    +

    logical. Decide whether to include text variables in the +compare of categorical data. The default value is FALSE, which also not includes character variables.

    + -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    .data

    an object of class "split_df", usually, a result of a call to split_df().

    add_character

    logical. Decide whether to include text variables in the -compare of categorical data. The default value is FALSE, which also not includes character variables.

    uniq_thres

    numeric. Set a threshold to removing variables when the ratio of unique values(number of unique values / number of observation) is greater than the set value.

    miss_msg

    logical. Set whether to output a message when diagnosing missing value.

    verbose

    logical. Set whether to echo information to the console at runtime.

    +
    uniq_thres
    +

    numeric. Set a threshold to removing variables when the ratio of unique values(number of unique values / number of observation) is greater than the set value.

    -

    Value

    -

    list. -Variables of tbl_df for first component named "single_value":

      -

    • variables : character. variable name

      • +
      miss_msg
      +

      logical. Set whether to output a message when diagnosing missing value.

      + + +
      verbose
      +

      logical. Set whether to echo information to the console at runtime.

      + +
    +
    +

    Value

    + + +

    list. +Variables of tbl_df for first component named "single_value":

    • variables : character. variable name

    • train_uniq : character. the type of unique value in train set. it is divided into "single" and "multi".

    • test_uniq : character. the type of unique value in test set. it is divided into "single" and "multi".

      • -
    - -

    Variables of tbl_df for second component named "uniq_rate":

      -

    • variables : character. categorical variable name

      • +

    Variables of tbl_df for second component named "uniq_rate":

    • variables : character. categorical variable name

    • train_uniqcount : numeric. the number of unique value in train set

    • train_uniqrate : numeric. the ratio of unique values(number of unique values / number of observation) in train set

    • test_uniqcount : numeric. the number of unique value in test set

    • test_uniqrate : numeric. the ratio of unique values(number of unique values / number of observation) in test set

      • -
    - -

    Variables of tbl_df for third component named "missing_level":

      -

    • variables : character. variable name

      • +

    Variables of tbl_df for third component named "missing_level":

    • variables : character. variable name

    • n_levels : integer. count of level of categorical variable

    • train_missing_nlevel : integer. the number of non-existent levels in the train set

    • test_missing_nlevel : integer. he number of non-existent levels in the test set

      • -
    - -

    Details

    - +
    +
    +

    Details

    In the two split datasets, a variable with a single value, a variable with a level not found in any dataset, and a variable with a high ratio to the number of levels are diagnosed.

    +
    -

    Examples

    - 
    library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
    #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
    
-defaults <- ISLR::Default
-defaults$id <- seq(NROW(defaults))
-
-set.seed(1)
-defaults[sample(seq(NROW(defaults)), 3), "student"] <- NA
-set.seed(2)
-defaults[sample(seq(NROW(defaults)), 10), "balance"] <- NA
-
-sb <- defaults %>%
-  split_by(default)
-
-sb %>%
-  compare_diag()
    #> * Detected diagnose missing value
    #>  - student
-#>  - balance
-#>  - balance
    #> 
-#> * Detected diagnose missing levels
    #>  - student
    #> $missing_value
-#> # A tibble: 3 x 4
-#>   variables train_misscount train_missrate test_missrate
-#>   <chr>               <int>          <dbl>         <dbl>
-#> 1 student                 3         0.0429       NA     
-#> 2 balance                 8         0.114        NA     
-#> 3 balance                 2        NA             0.0667
-#> 
-#> $single_value
-#> # A tibble: 0 x 3
-#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
-#> 
-#> $uniq_rate
-#> # A tibble: 0 x 5
-#> # … with 5 variables: variables <chr>, train_uniqcount <int>,
-#> #   train_uniqrate <dbl>, test_uniqcount <int>, test_uniqrate <dbl>
-#> 
-#> $missing_level
-#> # A tibble: 1 x 4
-#>   variables n_levels train_missing_nlevel test_missing_nlevel
-#>   <chr>        <int>                <int>               <int>
-#> 1 student          3                    0                   1
-#> 
    
-sb %>%
-  compare_diag(add_character = TRUE)
    #> * Detected diagnose missing value
    #>  - student
-#>  - balance
-#>  - balance
    #> 
-#> * Detected diagnose missing levels
    #>  - student
    #> $missing_value
-#> # A tibble: 3 x 4
-#>   variables train_misscount train_missrate test_missrate
-#>   <chr>               <int>          <dbl>         <dbl>
-#> 1 student                 3         0.0429       NA     
-#> 2 balance                 8         0.114        NA     
-#> 3 balance                 2        NA             0.0667
-#> 
-#> $single_value
-#> # A tibble: 0 x 3
-#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
-#> 
-#> $uniq_rate
-#> # A tibble: 0 x 5
-#> # … with 5 variables: variables <chr>, train_uniqcount <int>,
-#> #   train_uniqrate <dbl>, test_uniqcount <int>, test_uniqrate <dbl>
-#> 
-#> $missing_level
-#> # A tibble: 1 x 4
-#>   variables n_levels train_missing_nlevel test_missing_nlevel
-#>   <chr>        <int>                <int>               <int>
-#> 1 student          3                    0                   1
-#> 
    
-sb %>%
-  compare_diag(uniq_thres = 0.0005)
    #> * Detected diagnose missing value
    #>  - student
-#>  - balance
-#>  - balance
    #> 
-#> * Detected diagnose many unique value
    #>  - default
-#>  - student
    #> 
-#> * Detected diagnose missing levels
    #>  - student
    #> $missing_value
-#> # A tibble: 3 x 4
-#>   variables train_misscount train_missrate test_missrate
-#>   <chr>               <int>          <dbl>         <dbl>
-#> 1 student                 3         0.0429       NA     
-#> 2 balance                 8         0.114        NA     
-#> 3 balance                 2        NA             0.0667
-#> 
-#> $single_value
-#> # A tibble: 0 x 3
-#> # … with 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
-#> 
-#> $uniq_rate
-#> # A tibble: 2 x 5
-#>   variables train_uniqcount train_uniqrate test_uniqcount test_uniqrate
-#>   <chr>               <int>          <dbl>          <int>         <dbl>
-#> 1 default                NA             NA              2      0.000667
-#> 2 student                NA             NA              2      0.000667
-#> 
-#> $missing_level
-#> # A tibble: 1 x 4
-#>   variables n_levels train_missing_nlevel test_missing_nlevel
-#>   <chr>        <int>                <int>               <int>
-#> 1 student          3                    0                   1
-#> 
    
-
    +
    +

    Examples

    + 
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+defaults <- ISLR::Default
+defaults$id <- seq(NROW(defaults))
+
+set.seed(1)
+defaults[sample(seq(NROW(defaults)), 3), "student"] <- NA
+set.seed(2)
+defaults[sample(seq(NROW(defaults)), 10), "balance"] <- NA
+
+sb <- defaults %>%
+  split_by(default)
+
+sb %>%
+  compare_diag()
+#> * Detected diagnose missing value
+#>  - student
+#>  - balance
+#>  - balance
+#> 
+#> * Detected diagnose missing levels
+#>  - student
+#> $missing_value
+#> # A tibble: 3 × 4
+#>   variables train_misscount train_missrate test_missrate
+#>   <chr>               <int>          <dbl>         <dbl>
+#> 1 student                 3         0.0429       NA     
+#> 2 balance                 8         0.114        NA     
+#> 3 balance                 2        NA             0.0667
+#> 
+#> $single_value
+#> # A tibble: 0 × 3
+#> # ℹ 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
+#> 
+#> $uniq_rate
+#> # A tibble: 0 × 5
+#> # ℹ 5 variables: variables <chr>, train_uniqcount <int>, train_uniqrate <dbl>,
+#> #   test_uniqcount <int>, test_uniqrate <dbl>
+#> 
+#> $missing_level
+#> # A tibble: 1 × 4
+#>   variables n_levels train_missing_nlevel test_missing_nlevel
+#>   <chr>        <int>                <int>               <int>
+#> 1 student          3                    0                   1
+#> 
+
+sb %>%
+  compare_diag(add_character = TRUE)
+#> * Detected diagnose missing value
+#>  - student
+#>  - balance
+#>  - balance
+#> 
+#> * Detected diagnose missing levels
+#>  - student
+#> $missing_value
+#> # A tibble: 3 × 4
+#>   variables train_misscount train_missrate test_missrate
+#>   <chr>               <int>          <dbl>         <dbl>
+#> 1 student                 3         0.0429       NA     
+#> 2 balance                 8         0.114        NA     
+#> 3 balance                 2        NA             0.0667
+#> 
+#> $single_value
+#> # A tibble: 0 × 3
+#> # ℹ 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
+#> 
+#> $uniq_rate
+#> # A tibble: 0 × 5
+#> # ℹ 5 variables: variables <chr>, train_uniqcount <int>, train_uniqrate <dbl>,
+#> #   test_uniqcount <int>, test_uniqrate <dbl>
+#> 
+#> $missing_level
+#> # A tibble: 1 × 4
+#>   variables n_levels train_missing_nlevel test_missing_nlevel
+#>   <chr>        <int>                <int>               <int>
+#> 1 student          3                    0                   1
+#> 
+
+sb %>%
+  compare_diag(uniq_thres = 0.0005)
+#> * Detected diagnose missing value
+#>  - student
+#>  - balance
+#>  - balance
+#> 
+#> * Detected diagnose many unique value
+#>  - default
+#>  - student
+#> 
+#> * Detected diagnose missing levels
+#>  - student
+#> $missing_value
+#> # A tibble: 3 × 4
+#>   variables train_misscount train_missrate test_missrate
+#>   <chr>               <int>          <dbl>         <dbl>
+#> 1 student                 3         0.0429       NA     
+#> 2 balance                 8         0.114        NA     
+#> 3 balance                 2        NA             0.0667
+#> 
+#> $single_value
+#> # A tibble: 0 × 3
+#> # ℹ 3 variables: variables <chr>, train_uniq <lgl>, test_uniq <lgl>
+#> 
+#> $uniq_rate
+#> # A tibble: 2 × 5
+#>   variables train_uniqcount train_uniqrate test_uniqcount test_uniqrate
+#>   <chr>               <int>          <dbl>          <int>         <dbl>
+#> 1 default                NA             NA              2      0.000667
+#> 2 student                NA             NA              2      0.000667
+#> 
+#> $missing_level
+#> # A tibble: 1 × 4
+#>   variables n_levels train_missing_nlevel test_missing_nlevel
+#>   <chr>        <int>                <int>               <int>
+#> 1 student          3                    0                   1
+#> 
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/compare_performance.html b/docs/reference/compare_performance.html index 31e55f8..c6c884d 100644 --- a/docs/reference/compare_performance.html +++ b/docs/reference/compare_performance.html @@ -1,5 +1,5 @@ -Compare model performance — compare_performance • alookrCompare model performance — compare_performance • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -59,17 +59,20 @@

    Compare model performance

    - 
    compare_performance(model)
    + 
    compare_performance(model)

    Arguments

    model

    A model_df. results of predicted model that created by run_predict().

    +

    Value

    -

    list. results of compared model performance. + + +

    list. results of compared model performance. list has the following components:

    • recommend_model : character. The name of the model that is recommended as the best among the various models.

    • top_count : numeric. The number of best performing performance metrics by model.

    • mean_rank : numeric. Average of ranking individual performance metrics by model.

      • @@ -91,28 +94,28 @@

      Value

      Examples

      - 
      # \donttest{
-library(dplyr)
-
-# Divide the train data set and the test data set.
-sb <- rpart::kyphosis %>%
-  split_by(Kyphosis)
-
-# Extract the train data set from original data set.
-train <- sb %>%
-  extract_set(set = "train")
-
-# Extract the test data set from original data set.
-test <- sb %>%
-  extract_set(set = "test")
-
-# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
-train <- sb %>%
-  sampling_target(seed = 1234L, method = "ubSMOTE")
-
-# Cleaning the set.
-train <- train %>%
-  cleanse
+    
      # \donttest{
+library(dplyr)
+
+# Divide the train data set and the test data set.
+sb <- rpart::kyphosis %>%
+  split_by(Kyphosis)
+
+# Extract the train data set from original data set.
+train <- sb %>%
+  extract_set(set = "train")
+
+# Extract the test data set from original data set.
+test <- sb %>%
+  extract_set(set = "test")
+
+# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
+train <- sb %>%
+  sampling_target(seed = 1234L, method = "ubSMOTE")
+
+# Cleaning the set.
+train <- train %>%
+  cleanse
 #> ── Checking unique value ─────────────────────────── unique value is one ──
 #> No variables that unique value is one.
 #> 
@@ -123,57 +126,20 @@ 
      Examples
      
 #> No character variables.
 #> 
 #> 
-
-# Run the model fitting.
-result <- run_models(.data = train, target = "Kyphosis", positive = "present")
-
-# Predict the model.
-pred <- run_predict(result, test)
-
-# Compare the model performance
-compare_performance(pred)
-#> $recommend_model
-#> [1] "ranger"
-#> 
-#> $top_metric_count
-#>     logistic        rpart        ctree randomForest       ranger      xgboost 
-#>            2            3            1            6           11            2 
-#>        lasso 
-#>            2 
-#> 
-#> $mean_rank
-#>     logistic        rpart        ctree randomForest       ranger      xgboost 
-#>     4.307692     4.846154     6.500000     2.730769     1.846154     3.384615 
-#>        lasso 
-#>     4.384615 
-#> 
-#> $top_metric
-#> $top_metric$logistic
-#> [1] "Precision"   "Specificity"
-#> 
-#> $top_metric$rpart
-#> [1] "Precision"   "Specificity" "LiftAUC"    
-#> 
-#> $top_metric$ctree
-#> [1] "Recall"
-#> 
-#> $top_metric$randomForest
-#> [1] "Recall"  "AUC"     "Gini"    "PRAUC"   "GainAUC" "KS_Stat"
-#> 
-#> $top_metric$ranger
-#>  [1] "ZeroOneLoss" "Accuracy"    "Precision"   "Recall"      "Specificity"
-#>  [6] "F1_Score"    "AUC"         "Gini"        "PRAUC"       "GainAUC"    
-#> [11] "KS_Stat"    
-#> 
-#> $top_metric$xgboost
-#> [1] "Recall"  "LogLoss"
-#> 
-#> $top_metric$lasso
-#> [1] "Precision"   "Specificity"
-#> 
-#> 
-# }
-
+
+# Run the model fitting.
+result <- run_models(.data = train, target = "Kyphosis", positive = "present")
+
+# Predict the model.
+pred <- run_predict(result, test)
+
+# Compare the model performance
+compare_performance(pred)
+#> Error in purrr::map(., ~future::value(.x)): ℹ In index: 1.
+#> Caused by error:
+#> ! object 'pred' not found
+# }
+
      @@ -188,8 +154,7 @@

      Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/compare_plot.html b/docs/reference/compare_plot.html index 3871abf..c54c66a 100644 --- a/docs/reference/compare_plot.html +++ b/docs/reference/compare_plot.html @@ -1,6 +1,6 @@ -Comparison plot of train set and test set — compare_plot • alookrComparison plot of train set and test set — compare_plot • alookr @@ -18,7 +18,7 @@ alookr - 0.3.7 + 0.3.9 @@ -61,13 +61,15 @@

    Comparison plot of train set and test set

    - 
    compare_plot(.data, ...)
    + 
    compare_plot(.data, ...)

    Arguments

    .data

    an object of class "split_df", usually, a result of a call to split_df().

    + +
    ...

    one or more unquoted expressions separated by commas. Select the variable you want to plotting. @@ -78,10 +80,13 @@

    Arguments

    These arguments are automatically quoted and evaluated in a context where column names represent column positions. They support unquoting and splicing.

    +

    Value

    -

    There is no return value. Draw only the plot.

    + + +

    There is no return value. Draw only the plot.

    Details

    @@ -91,10 +96,10 @@

    Details

    Examples

    - 
    library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
+    
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
 #>   default student   balance    income
 #> 1      No      No  729.5265 44361.625
 #> 2      No     Yes  817.1804 12106.135
@@ -102,25 +107,18 @@ 
    Examples
    
 #> 4      No      No  529.2506 35704.494
 #> 5      No      No  785.6559 38463.496
 #> 6      No     Yes  919.5885  7491.559
-
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-sb %>%
-  compare_plot("income")
-
-
-sb %>%
-  compare_plot()
-#> Warning: `unite_()` was deprecated in tidyr 1.2.0.
-#> Please use `unite()` instead.
-#> This warning is displayed once every 8 hours.
-#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
-
-
-
-
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+sb %>%
+  compare_plot("income")
+#> Error in plot_compare(.data, x): object 'sb' not found
+
+sb %>%
+  compare_plot()
+#> Error in plot_compare(.data, x): object 'sb' not found
    @@ -135,8 +133,7 @@

    Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/compare_target_category.html b/docs/reference/compare_target_category.html index ba32d7d..2df0c6f 100644 --- a/docs/reference/compare_target_category.html +++ b/docs/reference/compare_target_category.html @@ -1,68 +1,13 @@ - - - - - - - -Comparison of categorical variables of train set and test set — compare_target_category • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Comparison of categorical variables of train set and test set — compare_target_category • alookr - + + - - - -
    -
    - -
    - -
    +

    Comparison of categorical variables of train set and test set

    - Source: R/split.R + Source: R/split.R
    @@ -122,18 +60,18 @@

    Comparison of categorical variables of train set and test set

    the train set and test set included in the "split_df" class.

    - 
    compare_target_category(.data, ..., add_character = FALSE, margin = FALSE)
    +
    + 
    compare_target_category(.data, ..., add_character = FALSE, margin = FALSE)
    +
    + +
    +

    Arguments

    +
    .data
    +

    an object of class "split_df", usually, a result of a call to split_df().

    + -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    .data

    an object of class "split_df", usually, a result of a call to split_df().

    ...

    one or more unquoted expressions separated by commas. +

    ...
    +

    one or more unquoted expressions separated by commas. Select the categorical variable you want to compare. You can treat variable names like they are positions. Positive values select variables; negative values to drop variables. @@ -141,116 +79,126 @@

    Arg start with all variables. These arguments are automatically quoted and evaluated in a context where column names represent column positions. -They support unquoting and splicing.

    add_character

    logical. Decide whether to include text variables in the -compare of categorical data. The default value is FALSE, which also not includes character variables.

    margin

    logical. Choose to calculate the marginal frequency information.

    +They support unquoting and splicing.

    + + +
    add_character
    +

    logical. Decide whether to include text variables in the +compare of categorical data. The default value is FALSE, which also not includes character variables.

    + -

    Value

    +
    margin
    +

    logical. Choose to calculate the marginal frequency information.

    -

    tbl_df. -Variables of tbl_df for comparison:

      -

    • variable : character. categorical variable name

      • +
    +
    +

    Value

    + + +

    tbl_df. +Variables of tbl_df for comparison:

    • variable : character. categorical variable name

    • level : factor. level of categorical variables

    • train : numeric. the relative frequency of the level in the train set

    • test : numeric. the relative frequency of the level in the test set

    • abs_diff : numeric. the absolute value of the difference between two relative frequencies

      • -
    - -

    Details

    - +
    +
    +

    Details

    Compare the statistics of the numerical variables of the train set and the test set to determine whether the raw data is well separated into two data sets.

    +
    -

    Examples

    - 
    library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
    #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
    
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-sb %>%
-  compare_target_category()
    #> # A tibble: 4 x 5
-#>   variable level train  test abs_diff
-#>   <chr>    <fct> <dbl> <dbl>    <dbl>
-#> 1 default  No    96.7  96.7   0.00476
-#> 2 default  Yes    3.33  3.33  0.00476
-#> 3 student  No    70.6  70.4   0.181  
-#> 4 student  Yes   29.4  29.6   0.181  
    
-sb %>%
-  compare_target_category(add_character = TRUE)
    #> # A tibble: 4 x 5
-#>   variable level train  test abs_diff
-#>   <chr>    <fct> <dbl> <dbl>    <dbl>
-#> 1 default  No    96.7  96.7   0.00476
-#> 2 default  Yes    3.33  3.33  0.00476
-#> 3 student  No    70.6  70.4   0.181  
-#> 4 student  Yes   29.4  29.6   0.181  
    
-sb %>%
-  compare_target_category(margin = TRUE)
    #> # A tibble: 6 x 5
-#>   variable level    train   test abs_diff
-#>   <chr>    <fct>    <dbl>  <dbl>    <dbl>
-#> 1 default  No       96.7   96.7   0.00476
-#> 2 default  Yes       3.33   3.33  0.00476
-#> 3 default  <Total> 100    100     0.00952
-#> 4 student  No       70.6   70.4   0.181  
-#> 5 student  Yes      29.4   29.6   0.181  
-#> 6 student  <Total> 100    100     0.362  
    
-sb %>%
-  compare_target_category(student)
    #> # A tibble: 2 x 5
-#>   variable level train  test abs_diff
-#>   <chr>    <fct> <dbl> <dbl>    <dbl>
-#> 1 student  No     70.6  70.4    0.181
-#> 2 student  Yes    29.4  29.6    0.181
    
-sb %>%
-  compare_target_category(student, margin = TRUE)
    #> # A tibble: 3 x 5
-#>   variable level   train  test abs_diff
-#>   <chr>    <fct>   <dbl> <dbl>    <dbl>
-#> 1 student  No       70.6  70.4    0.181
-#> 2 student  Yes      29.4  29.6    0.181
-#> 3 student  <Total> 100   100      0.362
    
-
    +
    +

    Examples

    + 
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+sb %>%
+  compare_target_category()
+#> # A tibble: 4 × 5
+#>   variable level train  test abs_diff
+#>   <chr>    <fct> <dbl> <dbl>    <dbl>
+#> 1 default  No    96.7  96.7   0.00476
+#> 2 default  Yes    3.33  3.33  0.00476
+#> 3 student  No    70.3  71.1   0.724  
+#> 4 student  Yes   29.7  28.9   0.724  
+
+sb %>%
+  compare_target_category(add_character = TRUE)
+#> # A tibble: 4 × 5
+#>   variable level train  test abs_diff
+#>   <chr>    <fct> <dbl> <dbl>    <dbl>
+#> 1 default  No    96.7  96.7   0.00476
+#> 2 default  Yes    3.33  3.33  0.00476
+#> 3 student  No    70.3  71.1   0.724  
+#> 4 student  Yes   29.7  28.9   0.724  
+
+sb %>%
+  compare_target_category(margin = TRUE)
+#> # A tibble: 6 × 5
+#>   variable level    train   test abs_diff
+#>   <chr>    <fct>    <dbl>  <dbl>    <dbl>
+#> 1 default  No       96.7   96.7   0.00476
+#> 2 default  Yes       3.33   3.33  0.00476
+#> 3 default  <Total> 100    100     0.00952
+#> 4 student  No       70.3   71.1   0.724  
+#> 5 student  Yes      29.7   28.9   0.724  
+#> 6 student  <Total> 100    100     1.45   
+
+sb %>%
+  compare_target_category(student)
+#> # A tibble: 2 × 5
+#>   variable level train  test abs_diff
+#>   <chr>    <fct> <dbl> <dbl>    <dbl>
+#> 1 student  No     70.3  71.1    0.724
+#> 2 student  Yes    29.7  28.9    0.724
+
+sb %>%
+  compare_target_category(student, margin = TRUE)
+#> # A tibble: 3 × 5
+#>   variable level   train  test abs_diff
+#>   <chr>    <fct>   <dbl> <dbl>    <dbl>
+#> 1 student  No       70.3  71.1    0.724
+#> 2 student  Yes      29.7  28.9    0.724
+#> 3 student  <Total> 100   100      1.45 
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/compare_target_numeric.html b/docs/reference/compare_target_numeric.html index 88ee7f7..6b4c07b 100644 --- a/docs/reference/compare_target_numeric.html +++ b/docs/reference/compare_target_numeric.html @@ -1,68 +1,13 @@ - - - - - - - -Comparison of numerical variables of train set and test set — compare_target_numeric • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Comparison of numerical variables of train set and test set — compare_target_numeric • alookr - - - - + + -
    -
    - -
    - -
    +

    Comparison of numerical variables of train set and test set

    - Source: R/split.R + Source: R/split.R
    @@ -122,18 +60,18 @@

    Comparison of numerical variables of train set and test set

    the train set and test set included in the "split_df" class.

    - 
    compare_target_numeric(.data, ...)
    - -

    Arguments

    - - - - - - - - - - -
    .data

    an object of class "split_df", usually, a result of a call to split_df().

    ...

    one or more unquoted expressions separated by commas. +

    + 
    compare_target_numeric(.data, ...)
    +
    + +
    +

    Arguments

    +
    .data
    +

    an object of class "split_df", usually, a result of a call to split_df().

    + + +
    ...
    +

    one or more unquoted expressions separated by commas. Select the numeric variable you want to compare. You can treat variable names like they are positions. Positive values select variables; negative values to drop variables. @@ -141,15 +79,15 @@

    Arg start with all variables. These arguments are automatically quoted and evaluated in a context where column names represent column positions. -They support unquoting and splicing.

    +They support unquoting and splicing.

    -

    Value

    +
    +
    +

    Value

    + -

    tbl_df. -Variables for comparison:

      -

    • variable : character. numeric variable name

      • +

      tbl_df. +Variables for comparison:

      • variable : character. numeric variable name

      • train_mean : numeric. arithmetic mean of train set

      • test_mean : numeric. arithmetic mean of test set

      • train_sd : numeric. standard deviation of train set

        • @@ -158,65 +96,69 @@

        Value

        the standard deviation

      • test_z : numeric. the arithmetic mean of the test set divided by the standard deviation

        • -
      - -

      Details

      - +
    +
    +

    Details

    Compare the statistics of the numerical variables of the train set and the test set to determine whether the raw data is well separated into two data sets.

    +
    -

    Examples

    - 
    library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
    #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
    
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-sb %>%
-  compare_target_numeric()
    #> # A tibble: 2 x 7
-#>   variable train_mean test_mean train_sd test_sd train_z test_z
-#>   <chr>         <dbl>     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
-#> 1 balance        834.      839.     485.    481.    1.72   1.74
-#> 2 income       33361.    33881.   13331.  13344.    2.50   2.54
    
-sb %>%
-  compare_target_numeric(balance)
    #> # A tibble: 1 x 7
-#>   variable train_mean test_mean train_sd test_sd train_z test_z
-#>   <chr>         <dbl>     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
-#> 1 balance        834.      839.     485.    481.    1.72   1.74
    
-
    +
    +

    Examples

    + 
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+sb %>%
+  compare_target_numeric()
+#> # A tibble: 2 × 7
+#>   variable train_mean test_mean train_sd test_sd train_z test_z
+#>   <chr>         <dbl>     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
+#> 1 balance        837.      832.     486.    478.    1.72   1.74
+#> 2 income       33466.    33637.   13353.  13301.    2.51   2.53
+
+sb %>%
+  compare_target_numeric(balance)
+#> # A tibble: 1 × 7
+#>   variable train_mean test_mean train_sd test_sd train_z test_z
+#>   <chr>         <dbl>     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
+#> 1 balance        837.      832.     486.    478.    1.72   1.74
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/extract_set.html b/docs/reference/extract_set.html index 0107dad..e5e2aef 100644 --- a/docs/reference/extract_set.html +++ b/docs/reference/extract_set.html @@ -1,67 +1,12 @@ - - - - - - - -Extract train/test dataset — extract_set • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Extract train/test dataset — extract_set • alookr - + + - - - -
    -
    - -
    - -
    +

    Extract train/test dataset

    - Source: R/split.R + Source: R/split.R
    @@ -120,74 +58,79 @@

    Extract train/test dataset

    Extract train set or test set from split_df class object

    - 
    extract_set(x, set = c("train", "test"))
    +
    + 
    extract_set(x, set = c("train", "test"))
    +
    + +
    +

    Arguments

    +
    x
    +

    an object of class "split_df", usually, a result of a call to split_df().

    -

    Arguments

    - - - - - - - - - - -
    x

    an object of class "split_df", usually, a result of a call to split_df().

    set

    character. Specifies whether the extracted data is a train set or a test set. -You can use "train" or "test".

    -

    Value

    +
    set
    +

    character. Specifies whether the extracted data is a train set or a test set. +You can use "train" or "test".

    -

    an object of class "tbl_df".

    -

    Details

    +
    +
    +

    Value

    + +

    an object of class "tbl_df".

    +
    +
    +

    Details

    Extract the train or test sets based on the parameters you defined when creating split_df with split_by().

    +
    -

    Examples

    - 
    library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
    #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
    
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-train <- sb %>%
-  extract_set(set = "train")
-
-test <- sb %>%
-  extract_set(set = "test")
    +
    +

    Examples

    + 
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+train <- sb %>%
+  extract_set(set = "train")
+
+test <- sb %>%
+  extract_set(set = "test")
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
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z*eq1Bv*v;)ef_;!XHYs;*5i&=_6Il&9v*DBT#Qg{Z8_NPfe6UB3v;Mym;Eo=jdJ_0 zM&dIry1JV4{V{Lq8Zjz)0Iw+v#z$kS91NL=6lrXlY5qd8o4brJ8k`V-HS^#BpMJL=R3ksH})n7i#rb1N-;|(hcalmG27?t%;}-v(EfaLIF!6XubJDDxtKXYp&$@ zyv;eTc?d=eGI;~h^bwkzMnMK;pfk;gDi0%bnJmmL*8eDllRGVi5F9~3%-BG^7B-Yt Vu;;n{BnAim$x13pl!)ni{Vz;7YJUI# literal 0 HcmV?d00001 diff --git a/docs/reference/index.html b/docs/reference/index.html index 904a458..d828c63 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -1,5 +1,5 @@ -Function reference • alookrFunction reference • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -54,8 +54,7 @@

    Reference

    Model Classifier for Binary Classification

    -

    A collection of tools that support data cleansing and splitting, -predictive modeling, and model evaluation.

    +

    A collection of tools that support data cleansing and splitting, predictive modeling, and model evaluation.

    Cleansing the dataset

    @@ -158,8 +157,7 @@

    Classification Modeling -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/matthews.html b/docs/reference/matthews.html index 53bd107..b4f792e 100644 --- a/docs/reference/matthews.html +++ b/docs/reference/matthews.html @@ -1,67 +1,12 @@ - - - - - - - -Compute Matthews Correlation Coefficient — matthews • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Compute Matthews Correlation Coefficient — matthews • alookr - - + + - - -
    -
    - -
    - -
    +

    Compute Matthews Correlation Coefficient

    - Source: R/evaluate.R + Source: R/evaluate.R
    @@ -120,83 +58,92 @@

    Compute Matthews Correlation Coefficient

    compute the Matthews correlation coefficient with actual and predict values.

    - 
    matthews(predicted, y, positive)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    predicted

    numeric. the predicted value of binary classification

    y

    factor or character. the actual value of binary classification

    positive

    level of positive class of binary classification

    - -

    Value

    - -

    numeric. The Matthews Correlation Coefficient.

    -

    Details

    +
    + 
    matthews(predicted, y, positive)
    +
    + +
    +

    Arguments

    +
    predicted
    +

    numeric. the predicted value of binary classification

    + +
    y
    +

    factor or character. the actual value of binary classification

    + + +
    positive
    +

    level of positive class of binary classification

    + +
    +
    +

    Value

    + + +

    numeric. The Matthews Correlation Coefficient.

    +
    +
    +

    Details

    The Matthews Correlation Coefficient has a value between -1 and 1, and the closer to 1, the better the performance of the binary classification.

    +
    -

    Examples

    - 
    # simulate actual data
-set.seed(123L)
-actual <- sample(c("Y", "N"), size = 100, prob = c(0.3, 0.7), replace = TRUE)
-actual
    #>   [1] "N" "Y" "N" "Y" "Y" "N" "N" "Y" "N" "N" "Y" "N" "N" "N" "N" "Y" "N" "N"
-#>  [19] "N" "Y" "Y" "N" "N" "Y" "N" "Y" "N" "N" "N" "N" "Y" "Y" "N" "Y" "N" "N"
-#>  [37] "Y" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "N" "N" "Y" "N"
-#>  [55] "N" "N" "N" "Y" "Y" "N" "N" "N" "N" "N" "Y" "N" "Y" "Y" "Y" "N" "Y" "N"
-#>  [73] "Y" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "N" "N" "Y" "Y" "Y" "N"
-#>  [91] "N" "N" "N" "N" "N" "N" "Y" "N" "N" "N"
    
-# simulate predict data
-set.seed(123L)
-pred <- sample(c("Y", "N"), size = 100, prob = c(0.2, 0.8), replace = TRUE)
-pred
    #>   [1] "N" "N" "N" "Y" "Y" "N" "N" "Y" "N" "N" "Y" "N" "N" "N" "N" "Y" "N" "N"
-#>  [19] "N" "Y" "Y" "N" "N" "Y" "N" "N" "N" "N" "N" "N" "Y" "Y" "N" "N" "N" "N"
-#>  [37] "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "N" "N" "N" "N"
-#>  [55] "N" "N" "N" "N" "Y" "N" "N" "N" "N" "N" "Y" "N" "Y" "Y" "N" "N" "N" "N"
-#>  [73] "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "Y" "Y" "N"
-#>  [91] "N" "N" "N" "N" "N" "N" "N" "N" "N" "N"
    
-# simulate confusion matrix
-table(pred, actual)
    #>     actual
-#> pred  N  Y
-#>    N 71 11
-#>    Y  0 18
    
-matthews(pred, actual, "Y")
    #> [1] 0.7330937
    +
    +

    Examples

    + 
    # simulate actual data
+set.seed(123L)
+actual <- sample(c("Y", "N"), size = 100, prob = c(0.3, 0.7), replace = TRUE)
+actual
+#>   [1] "N" "Y" "N" "Y" "Y" "N" "N" "Y" "N" "N" "Y" "N" "N" "N" "N" "Y" "N" "N"
+#>  [19] "N" "Y" "Y" "N" "N" "Y" "N" "Y" "N" "N" "N" "N" "Y" "Y" "N" "Y" "N" "N"
+#>  [37] "Y" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "N" "N" "Y" "N"
+#>  [55] "N" "N" "N" "Y" "Y" "N" "N" "N" "N" "N" "Y" "N" "Y" "Y" "Y" "N" "Y" "N"
+#>  [73] "Y" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "N" "N" "Y" "Y" "Y" "N"
+#>  [91] "N" "N" "N" "N" "N" "N" "Y" "N" "N" "N"
+
+# simulate predict data
+set.seed(123L)
+pred <- sample(c("Y", "N"), size = 100, prob = c(0.2, 0.8), replace = TRUE)
+pred
+#>   [1] "N" "N" "N" "Y" "Y" "N" "N" "Y" "N" "N" "Y" "N" "N" "N" "N" "Y" "N" "N"
+#>  [19] "N" "Y" "Y" "N" "N" "Y" "N" "N" "N" "N" "N" "N" "Y" "Y" "N" "N" "N" "N"
+#>  [37] "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "N" "N" "N" "N"
+#>  [55] "N" "N" "N" "N" "Y" "N" "N" "N" "N" "N" "Y" "N" "Y" "Y" "N" "N" "N" "N"
+#>  [73] "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "N" "Y" "Y" "Y" "N"
+#>  [91] "N" "N" "N" "N" "N" "N" "N" "N" "N" "N"
+
+# simulate confusion matrix
+table(pred, actual)
+#>     actual
+#> pred  N  Y
+#>    N 71 11
+#>    Y  0 18
+
+matthews(pred, actual, "Y")
+#> [1] 0.7330937
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/performance_metric.html b/docs/reference/performance_metric.html index 7735a51..96264ef 100644 --- a/docs/reference/performance_metric.html +++ b/docs/reference/performance_metric.html @@ -1,5 +1,5 @@ -Calculate metrics for model evaluation — performance_metric • alookrCalculate metrics for model evaluation — performance_metric • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -59,36 +59,49 @@

    Calculate metrics for model evaluation

    - 
    performance_metric(
-  pred,
-  actual,
-  positive,
-  metric = c("ZeroOneLoss", "Accuracy", "Precision", "Recall", "Sensitivity",
-    "Specificity", "F1_Score", "Fbeta_Score", "LogLoss", "AUC", "Gini", "PRAUC",
-    "LiftAUC", "GainAUC", "KS_Stat", "ConfusionMatrix"),
-  cutoff = 0.5,
-  beta = 1
-)
    + 
    performance_metric(
+  pred,
+  actual,
+  positive,
+  metric = c("ZeroOneLoss", "Accuracy", "Precision", "Recall", "Sensitivity",
+    "Specificity", "F1_Score", "Fbeta_Score", "LogLoss", "AUC", "Gini", "PRAUC",
+    "LiftAUC", "GainAUC", "KS_Stat", "ConfusionMatrix"),
+  cutoff = 0.5,
+  beta = 1
+)

    Arguments

    pred

    numeric. Probability values that predicts the positive class of the target variable.

    + +
    actual

    factor. The value of the actual target variable.

    + +
    positive

    character. Level of positive class of binary classification.

    + +
    metric

    character. The performance metrics you want to calculate. See details.

    + +
    cutoff

    numeric. Threshold for classifying predicted probability values into positive and negative classes.

    + +
    beta

    numeric. Weight of precision in harmonic mean for F-Beta Score.

    +

    Value

    -

    numeric or table object. + + +

    numeric or table object. Confusion Matrix return by table object. and otherwise is numeric.: The performance metrics calculated are as follows.:

    • ZeroOneLoss : Normalized Zero-One Loss(Classification Error Loss).

    • Accuracy : Accuracy.

      • @@ -115,28 +128,27 @@

      Details

      Examples

      - 
      # \donttest{
-library(dplyr)
-
-# Divide the train data set and the test data set.
-sb <- rpart::kyphosis %>%
-  split_by(Kyphosis)
-
-# Extract the train data set from original data set.
-train <- sb %>%
-  extract_set(set = "train")
-
-# Extract the test data set from original data set.
-test <- sb %>%
-  extract_set(set = "test")
-
-# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
-train <- sb %>%
-  sampling_target(seed = 1234L, method = "ubSMOTE")
-
-# Cleaning the set.
-train <- train %>%
-  cleanse
+    
      library(dplyr)
+
+# Divide the train data set and the test data set.
+sb <- rpart::kyphosis %>%
+  split_by(Kyphosis)
+
+# Extract the train data set from original data set.
+train <- sb %>%
+  extract_set(set = "train")
+
+# Extract the test data set from original data set.
+test <- sb %>%
+  extract_set(set = "test")
+
+# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
+train <- sb %>%
+  sampling_target(seed = 1234L, method = "ubSMOTE")
+
+# Cleaning the set.
+train <- train %>%
+  cleanse
 #> ── Checking unique value ─────────────────────────── unique value is one ──
 #> No variables that unique value is one.
 #> 
@@ -147,10 +159,10 @@ 
      Examples
      
 #> No character variables.
 #> 
 #> 
-
-# Run the model fitting.
-result <- run_models(.data = train, target = "Kyphosis", positive = "present")
-result
+
+# Run the model fitting.
+result <- run_models(.data = train, target = "Kyphosis", positive = "present")
+result
 #> # A tibble: 7 × 7
 #>   step     model_id     target   is_factor positive negative fitted_model
 #>   <chr>    <chr>        <chr>    <lgl>     <chr>    <chr>    <list>      
@@ -161,41 +173,40 @@ 
      Examples
      
 #> 5 1.Fitted ranger       Kyphosis TRUE      present  absent   <ranger>    
 #> 6 1.Fitted xgboost      Kyphosis TRUE      present  absent   <xgb.Bstr>  
 #> 7 1.Fitted lasso        Kyphosis TRUE      present  absent   <lognet>    
-
-# Predict the model.
-pred <- run_predict(result, test)
-pred
+
+# Predict the model.
+pred <- run_predict(result, test)
+pred
 #> # A tibble: 7 × 8
-#>   step        model_id target is_factor positive negative fitted_model predicted
-#>   <chr>       <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>   
-#> 1 2.Predicted logistic Kypho… TRUE      present  absent   <glm>        <fct>    
-#> 2 2.Predicted rpart    Kypho… TRUE      present  absent   <rpart>      <fct>    
-#> 3 2.Predicted ctree    Kypho… TRUE      present  absent   <BinaryTr>   <fct>    
-#> 4 2.Predicted randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <fct>    
-#> 5 2.Predicted ranger   Kypho… TRUE      present  absent   <ranger>     <fct>    
-#> 6 2.Predicted xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <fct>    
-#> 7 2.Predicted lasso    Kypho… TRUE      present  absent   <lognet>     <fct>    
-
-# Calculate Accuracy.
-performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis,
-  "present", "Accuracy")
-#> [1] 0.7083333
-# Calculate Confusion Matrix.
-performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis,
-  "present", "ConfusionMatrix")
+#>   step       model_id target is_factor positive negative fitted_model predicted 
+#>   <chr>      <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>    
+#> 1 2.Predict… logistic Kypho… TRUE      present  absent   <glm>        <prdct_cl>
+#> 2 2.Predict… rpart    Kypho… TRUE      present  absent   <rpart>      <prdct_cl>
+#> 3 2.Predict… ctree    Kypho… TRUE      present  absent   <BinaryTr>   <prdct_cl>
+#> 4 2.Predict… randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <prdct_cl>
+#> 5 2.Predict… ranger   Kypho… TRUE      present  absent   <ranger>     <prdct_cl>
+#> 6 2.Predict… xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <prdct_cl>
+#> 7 2.Predict… lasso    Kypho… TRUE      present  absent   <lognet>     <prdct_cl>
+
+# Calculate Accuracy.
+performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis,
+  "present", "Accuracy")
+#> [1] 0.5833333
+# Calculate Confusion Matrix.
+performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis,
+  "present", "ConfusionMatrix")
 #>          actual
 #> predict   absent present
-#>   absent      15       3
-#>   present      4       2
-# Calculate Confusion Matrix by cutoff = 0.55.
-performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis,
-  "present", "ConfusionMatrix", cutoff = 0.55)
+#>   absent       9       0
+#>   present     10       5
+# Calculate Confusion Matrix by cutoff = 0.55.
+performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis,
+  "present", "ConfusionMatrix", cutoff = 0.55)
 #>          actual
 #> predict   absent present
-#>   absent      15       3
-#>   present      4       2
-# }
-   
+#>   absent       9       0
+#>   present     10       5
+
      @@ -210,8 +221,7 @@

      Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

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b/docs/reference/plot_cutoff.html index 1b1f4de..10b906f 100644 --- a/docs/reference/plot_cutoff.html +++ b/docs/reference/plot_cutoff.html @@ -1,7 +1,7 @@ -Visualization for cut-off selection — plot_cutoff • alookrVisualization for cut-off selection — plot_cutoff • alookr @@ -19,7 +19,7 @@ alookr - 0.3.7 + 0.3.9 @@ -63,35 +63,46 @@

    Visualization for cut-off selection

    - 
    plot_cutoff(
-  predicted,
-  y,
-  positive,
-  type = c("mcc", "density", "prob"),
-  measure = c("mcc", "cross", "half")
-)
    + 
    plot_cutoff(
+  predicted,
+  y,
+  positive,
+  type = c("mcc", "density", "prob"),
+  measure = c("mcc", "cross", "half")
+)

    Arguments

    predicted

    numeric. the predicted value of binary classification

    + +
    y

    factor or character. the actual value of binary classification

    + +
    positive

    level of positive class of binary classification

    + +
    type

    character. Visualization type. "mcc" draw the Matthews Correlation Coefficient scatter plot, "density" draw the density plot of negative and positive, and "prob" draws line or points plots of the predicted probability.

    + +
    measure

    character. The kind of measure that calculates the cutoff. "mcc" is the Matthews Correlation Coefficient, "cross" is the point where the positive and negative densities cross, and "half" is the median of the probability, 0.5

    +

    Value

    -

    numeric. cut-off value

    + + +

    numeric. cut-off value

    Details

    @@ -102,38 +113,36 @@

    Details

    Examples

    - 
    # \donttest{
-library(ggplot2)
-library(rpart)
-data(kyphosis)
-
-fit <- glm(Kyphosis ~., family = binomial, kyphosis)
-pred <- predict(fit, type = "response")
-
-cutoff <- plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "mcc")
+    
    library(ggplot2)
+library(rpart)
+data(kyphosis)
+
+fit <- glm(Kyphosis ~., family = binomial, kyphosis)
+pred <- predict(fit, type = "response")
+
+cutoff <- plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "mcc")
 
-cutoff
+cutoff
 #> [1] 0.47
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "mcc", measure = "cross")
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "mcc", measure = "cross")
 
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "mcc", measure = "half")
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "mcc", measure = "half")
 
-
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "density", measure = "mcc")
+
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "density", measure = "mcc")
 
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "density", measure = "cross")
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "density", measure = "cross")
 
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "density", measure = "half")
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "density", measure = "half")
 
-
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "mcc")
+
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "mcc")
 
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "cross")
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "cross")
 
-plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "half")
+plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "half")
 
-# }
-
+
    @@ -148,8 +157,7 @@

    Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

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zLtKgR<8f;f!s}l@9=8}*Csv1xOa3hpUCsa8e`-oZn)5$@YtFC7+$TRq&xIn4PjP)a wPB`vgKNGtd56<}D&visJ|Lf=f_t(v=wv18rcDg4y*H%tBsG*d2;N-Rc0|$pF{Qv*} diff --git a/docs/reference/plot_performance.html b/docs/reference/plot_performance.html index fcbd4e4..b4b9ff9 100644 --- a/docs/reference/plot_performance.html +++ b/docs/reference/plot_performance.html @@ -1,5 +1,5 @@ -Visualization for ROC curve — plot_performance • alookrVisualization for ROC curve — plot_performance • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -59,17 +59,20 @@

    Visualization for ROC curve

    - 
    plot_performance(model)
    + 
    plot_performance(model)

    Arguments

    model

    A model_df. results of predicted model that created by run_predict().

    +

    Value

    -

    There is no return value. Only the plot is drawn.

    + + +

    There is no return value. Only the plot is drawn.

    Details

    @@ -78,28 +81,28 @@

    Details

    Examples

    - 
    # \donttest{
-library(dplyr)
-
-# Divide the train data set and the test data set.
-sb <- rpart::kyphosis %>%
-  split_by(Kyphosis)
-
-# Extract the train data set from original data set.
-train <- sb %>%
-  extract_set(set = "train")
-
-# Extract the test data set from original data set.
-test <- sb %>%
-  extract_set(set = "test")
-
-# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
-train <- sb %>%
-  sampling_target(seed = 1234L, method = "ubSMOTE")
-
-# Cleaning the set.
-train <- train %>%
-  cleanse
+    
    # \donttest{
+library(dplyr)
+
+# Divide the train data set and the test data set.
+sb <- rpart::kyphosis %>%
+  split_by(Kyphosis)
+
+# Extract the train data set from original data set.
+train <- sb %>%
+  extract_set(set = "train")
+
+# Extract the test data set from original data set.
+test <- sb %>%
+  extract_set(set = "test")
+
+# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
+train <- sb %>%
+  sampling_target(seed = 1234L, method = "ubSMOTE")
+
+# Cleaning the set.
+train <- train %>%
+  cleanse
 #> ── Checking unique value ─────────────────────────── unique value is one ──
 #> No variables that unique value is one.
 #> 
@@ -110,18 +113,18 @@ 
    Examples
    
 #> No character variables.
 #> 
 #> 
-
-# Run the model fitting.
-result <- run_models(.data = train, target = "Kyphosis", positive = "present")
-
-# Predict the model.
-pred <- run_predict(result, test)
-
-# Plot ROC curve
-plot_performance(pred)
+
+# Run the model fitting.
+result <- run_models(.data = train, target = "Kyphosis", positive = "present")
+
+# Predict the model.
+pred <- run_predict(result, test)
+
+# Plot ROC curve
+plot_performance(pred)
 
-# }
-
+# }
+
    @@ -136,8 +139,7 @@

    Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/run_models.html b/docs/reference/run_models.html index d7e006a..a13f499 100644 --- a/docs/reference/run_models.html +++ b/docs/reference/run_models.html @@ -1,5 +1,5 @@ -Fit binary classification model — run_models • alookrFit binary classification model — run_models • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -59,13 +59,12 @@

    Fit binary classification model

    - 
    run_models(
-  .data,
-  target,
-  positive,
-  models = c("logistic", "rpart", "ctree", "randomForest", "ranger", "xgboost",
-    "lasso")
-)
    + 
    run_models(
+  .data,
+  target,
+  positive,
+  models = c("logistic", "rpart", "ctree", "randomForest", "ranger", "xgboost", "lasso")
+)
    @@ -73,17 +72,26 @@

    Arguments

    .data

    A train_df. Train data to fit the model. It also supports tbl_df, tbl, and data.frame objects.

    + +
    target

    character. Name of target variable.

    + +
    positive

    character. Level of positive class of binary classification.

    + +
    models

    character. Algorithm types of model to fit. See details. default value is c("logistic", "rpart", "ctree", "randomForest", "ranger", "lasso").

    +

    Value

    -

    model_df. results of fitted model. + + +

    model_df. results of fitted model. model_df is composed of tbl_df and contains the following variables.:

    • step : character. The current stage in the model fit process. The result of calling run_models() is returned as "1.Fitted".

    • model_id : character. Type of fit model.

      • @@ -112,27 +120,27 @@

      Details

      Examples

      - 
      library(dplyr)
-
-# Divide the train data set and the test data set.
-sb <- rpart::kyphosis %>%
-  split_by(Kyphosis)
-
-# Extract the train data set from original data set.
-train <- sb %>%
-  extract_set(set = "train")
-
-# Extract the test data set from original data set.
-test <- sb %>%
-  extract_set(set = "test")
-
-# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
-train <- sb %>%
-  sampling_target(seed = 1234L, method = "ubSMOTE")
-
-# Cleaning the set.
-train <- train %>%
-  cleanse
+    
      library(dplyr)
+
+# Divide the train data set and the test data set.
+sb <- rpart::kyphosis %>%
+  split_by(Kyphosis)
+
+# Extract the train data set from original data set.
+train <- sb %>%
+  extract_set(set = "train")
+
+# Extract the test data set from original data set.
+test <- sb %>%
+  extract_set(set = "test")
+
+# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
+train <- sb %>%
+  sampling_target(seed = 1234L, method = "ubSMOTE")
+
+# Cleaning the set.
+train <- train %>%
+  cleanse
 #> ── Checking unique value ─────────────────────────── unique value is one ──
 #> No variables that unique value is one.
 #> 
@@ -143,10 +151,10 @@ 
      Examples
      
 #> No character variables.
 #> 
 #> 
-
-# Run the model fitting.
-result <- run_models(.data = train, target = "Kyphosis", positive = "present")
-result
+
+# Run the model fitting.
+result <- run_models(.data = train, target = "Kyphosis", positive = "present")
+result
 #> # A tibble: 7 × 7
 #>   step     model_id     target   is_factor positive negative fitted_model
 #>   <chr>    <chr>        <chr>    <lgl>     <chr>    <chr>    <list>      
@@ -157,10 +165,10 @@ 
      Examples
      
 #> 5 1.Fitted ranger       Kyphosis TRUE      present  absent   <ranger>    
 #> 6 1.Fitted xgboost      Kyphosis TRUE      present  absent   <xgb.Bstr>  
 #> 7 1.Fitted lasso        Kyphosis TRUE      present  absent   <lognet>    
-
-# Run the several kinds model fitting by dplyr
-train %>%
-  run_models(target = "Kyphosis", positive = "present")
+
+# Run the several kinds model fitting by dplyr
+train %>%
+  run_models(target = "Kyphosis", positive = "present")
 #> # A tibble: 7 × 7
 #>   step     model_id     target   is_factor positive negative fitted_model
 #>   <chr>    <chr>        <chr>    <lgl>     <chr>    <chr>    <list>      
@@ -171,14 +179,7 @@ 
      Examples
      
 #> 5 1.Fitted ranger       Kyphosis TRUE      present  absent   <ranger>    
 #> 6 1.Fitted xgboost      Kyphosis TRUE      present  absent   <xgb.Bstr>  
 #> 7 1.Fitted lasso        Kyphosis TRUE      present  absent   <lognet>    
-
-# Run the logistic model fitting by dplyr
-train %>%
-  run_models(target = "Kyphosis", positive = "present", models = "logistic")
-#> # A tibble: 1 × 7
-#>   step     model_id target   is_factor positive negative fitted_model
-#>   <chr>    <chr>    <chr>    <lgl>     <chr>    <chr>    <list>      
-#> 1 1.Fitted logistic Kyphosis TRUE      present  absent   <glm>       
+
      @@ -193,8 +194,7 @@

      Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/run_performance.html b/docs/reference/run_performance.html index 4960b1f..b336098 100644 --- a/docs/reference/run_performance.html +++ b/docs/reference/run_performance.html @@ -1,5 +1,5 @@ -Apply calculate performance metrics for model evaluation — run_performance • alookrApply calculate performance metrics for model evaluation — run_performance • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -59,19 +59,24 @@

    Apply calculate performance metrics for model evaluation

    - 
    run_performance(model, actual = NULL)
    + 
    run_performance(model, actual = NULL)

    Arguments

    model

    A model_df. results of predicted model that created by run_predict().

    + +
    actual

    factor. A data of target variable to evaluate the model. It supports factor that has binary class.

    +

    Value

    -

    model_df. results of predicted model. + + +

    model_df. results of predicted model. model_df is composed of tbl_df and contains the following variables.:

    • step : character. The current stage in the model fit process. The result of calling run_performance() is returned as "3.Performanced".

    • model_id : character. Type of fit model.

    • target : character. Name of target variable.

      • @@ -103,28 +108,28 @@

      Details

      Examples

      - 
      # \donttest{
-library(dplyr)
-
-# Divide the train data set and the test data set.
-sb <- rpart::kyphosis %>%
-  split_by(Kyphosis)
-
-# Extract the train data set from original data set.
-train <- sb %>%
-  extract_set(set = "train")
-
-# Extract the test data set from original data set.
-test <- sb %>%
-  extract_set(set = "test")
-
-# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
-train <- sb %>%
-  sampling_target(seed = 1234L, method = "ubSMOTE")
-
-# Cleaning the set.
-train <- train %>%
-  cleanse
+    
      # \donttest{
+library(dplyr)
+
+# Divide the train data set and the test data set.
+sb <- rpart::kyphosis %>%
+  split_by(Kyphosis)
+
+# Extract the train data set from original data set.
+train <- sb %>%
+  extract_set(set = "train")
+
+# Extract the test data set from original data set.
+test <- sb %>%
+  extract_set(set = "test")
+
+# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
+train <- sb %>%
+  sampling_target(seed = 1234L, method = "ubSMOTE")
+
+# Cleaning the set.
+train <- train %>%
+  cleanse
 #> ── Checking unique value ─────────────────────────── unique value is one ──
 #> No variables that unique value is one.
 #> 
@@ -135,10 +140,10 @@ 
      Examples
      
 #> No character variables.
 #> 
 #> 
-
-# Run the model fitting.
-result <- run_models(.data = train, target = "Kyphosis", positive = "present")
-result
+
+# Run the model fitting.
+result <- run_models(.data = train, target = "Kyphosis", positive = "present")
+result
 #> # A tibble: 7 × 7
 #>   step     model_id     target   is_factor positive negative fitted_model
 #>   <chr>    <chr>        <chr>    <lgl>     <chr>    <chr>    <list>      
@@ -149,213 +154,60 @@ 
      Examples
      
 #> 5 1.Fitted ranger       Kyphosis TRUE      present  absent   <ranger>    
 #> 6 1.Fitted xgboost      Kyphosis TRUE      present  absent   <xgb.Bstr>  
 #> 7 1.Fitted lasso        Kyphosis TRUE      present  absent   <lognet>    
-
-# Predict the model. (Case 1)
-pred <- run_predict(result, test)
-pred
+
+# Predict the model. (Case 1)
+pred <- run_predict(result, test)
+pred
 #> # A tibble: 7 × 8
-#>   step        model_id target is_factor positive negative fitted_model predicted
-#>   <chr>       <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>   
-#> 1 2.Predicted logistic Kypho… TRUE      present  absent   <glm>        <fct>    
-#> 2 2.Predicted rpart    Kypho… TRUE      present  absent   <rpart>      <fct>    
-#> 3 2.Predicted ctree    Kypho… TRUE      present  absent   <BinaryTr>   <fct>    
-#> 4 2.Predicted randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <fct>    
-#> 5 2.Predicted ranger   Kypho… TRUE      present  absent   <ranger>     <fct>    
-#> 6 2.Predicted xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <fct>    
-#> 7 2.Predicted lasso    Kypho… TRUE      present  absent   <lognet>     <fct>    
-
-# Calculate performace metrics. (Case 1)
-perf <- run_performance(pred)
-perf
-#> # A tibble: 7 × 7
-#>   step           model_id     target positive fitted_model predicted performance
-#>   <chr>          <chr>        <chr>  <chr>    <list>       <list>    <list>     
-#> 1 3.Performanced logistic     Kypho… present  <glm>        <fct>     <dbl [15]> 
-#> 2 3.Performanced rpart        Kypho… present  <rpart>      <fct>     <dbl [15]> 
-#> 3 3.Performanced ctree        Kypho… present  <BinaryTr>   <fct>     <dbl [15]> 
-#> 4 3.Performanced randomForest Kypho… present  <rndmFrs.>   <fct>     <dbl [15]> 
-#> 5 3.Performanced ranger       Kypho… present  <ranger>     <fct>     <dbl [15]> 
-#> 6 3.Performanced xgboost      Kypho… present  <xgb.Bstr>   <fct>     <dbl [15]> 
-#> 7 3.Performanced lasso        Kypho… present  <lognet>     <fct>     <dbl [15]> 
-perf$performance
-#> [[1]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08333333  0.91666667  1.00000000  0.60000000  0.60000000  1.00000000 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.75000000  0.75000000  0.22463025  0.93684211  0.87368421  0.68545455 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  2.20266453  0.84583333 80.00000000 
-#> 
-#> [[2]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08333333  0.91666667  1.00000000  0.60000000  0.60000000  1.00000000 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.75000000  0.75000000  0.34208129  0.80000000  0.64210526  0.24166667 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  2.53750000  0.73750000 60.00000000 
-#> 
-#> [[3]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>   0.1666667   0.8333333   0.5714286   0.8000000   0.8000000   0.8421053 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>   0.6666667   0.6666667   0.3977583   0.7894737   0.6210526   0.2722619 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>   1.5198810   0.7291667  64.2105263 
-#> 
-#> [[4]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08333333  0.91666667  0.80000000  0.80000000  0.80000000  0.94736842 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.80000000  0.80000000  0.22061290  0.98947368  0.97894737  0.76333333 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  2.33187386  0.88750000 94.73684211 
-#> 
-#> [[5]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.04166667  0.95833333  1.00000000  0.80000000  0.80000000  1.00000000 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.88888889  0.88888889  0.22338605  0.98947368  0.97894737  0.76333333 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  2.33179151  0.88750000 94.73684211 
-#> 
-#> [[6]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08333333  0.91666667  0.80000000  0.80000000  0.80000000  0.94736842 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.80000000  0.80000000  0.20825183  0.96842105  0.93684211  0.71964286 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  2.27712856  0.87083333 84.21052632 
-#> 
-#> [[7]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.08333333  0.91666667  1.00000000  0.60000000  0.60000000  1.00000000 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.75000000  0.75000000  0.22545040  0.93684211  0.87368421  0.68545455 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  2.20266453  0.84583333 80.00000000 
-#> 
-
-# Predict the model. (Case 2)
-pred <- run_predict(result, test[, -1])
-pred
+#>   step       model_id target is_factor positive negative fitted_model predicted 
+#>   <chr>      <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>    
+#> 1 2.Predict… logistic Kypho… TRUE      present  absent   <glm>        <prdct_cl>
+#> 2 2.Predict… rpart    Kypho… TRUE      present  absent   <rpart>      <prdct_cl>
+#> 3 2.Predict… ctree    Kypho… TRUE      present  absent   <BinaryTr>   <prdct_cl>
+#> 4 2.Predict… randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <prdct_cl>
+#> 5 2.Predict… ranger   Kypho… TRUE      present  absent   <ranger>     <prdct_cl>
+#> 6 2.Predict… xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <prdct_cl>
+#> 7 2.Predict… lasso    Kypho… TRUE      present  absent   <lognet>     <prdct_cl>
+
+# Calculate performace metrics. (Case 1)
+perf <- run_performance(pred)
+#> Error in purrr::map(., ~future::value(.x)): ℹ In index: 1.
+#> Caused by error:
+#> ! object 'pred' not found
+perf
+#> Error in eval(expr, envir, enclos): object 'perf' not found
+perf$performance
+#> Error in eval(expr, envir, enclos): object 'perf' not found
+
+# Predict the model. (Case 2)
+pred <- run_predict(result, test[, -1])
+pred
 #> # A tibble: 7 × 8
-#>   step        model_id target is_factor positive negative fitted_model predicted
-#>   <chr>       <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>   
-#> 1 2.Predicted logistic Kypho… TRUE      present  absent   <glm>        <fct>    
-#> 2 2.Predicted rpart    Kypho… TRUE      present  absent   <rpart>      <fct>    
-#> 3 2.Predicted ctree    Kypho… TRUE      present  absent   <BinaryTr>   <fct>    
-#> 4 2.Predicted randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <fct>    
-#> 5 2.Predicted ranger   Kypho… TRUE      present  absent   <ranger>     <fct>    
-#> 6 2.Predicted xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <fct>    
-#> 7 2.Predicted lasso    Kypho… TRUE      present  absent   <lognet>     <fct>    
-
-# Calculate performace metrics. (Case 2)
-perf <- run_performance(pred, pull(test[, 1]))
-perf
-#> # A tibble: 7 × 7
-#>   step           model_id     target positive fitted_model predicted performance
-#>   <chr>          <chr>        <chr>  <chr>    <list>       <list>    <list>     
-#> 1 3.Performanced logistic     Kypho… present  <glm>        <fct>     <dbl [15]> 
-#> 2 3.Performanced rpart        Kypho… present  <rpart>      <fct>     <dbl [15]> 
-#> 3 3.Performanced ctree        Kypho… present  <BinaryTr>   <fct>     <dbl [15]> 
-#> 4 3.Performanced randomForest Kypho… present  <rndmFrs.>   <fct>     <dbl [15]> 
-#> 5 3.Performanced ranger       Kypho… present  <ranger>     <fct>     <dbl [15]> 
-#> 6 3.Performanced xgboost      Kypho… present  <xgb.Bstr>   <fct>     <dbl [15]> 
-#> 7 3.Performanced lasso        Kypho… present  <lognet>     <fct>     <dbl [15]> 
-perf$performance
-#> [[1]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.25000000  0.75000000  0.33333333  0.20000000  0.20000000  0.89473684 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.25000000  0.25000000  0.76115737  0.48421053 -0.03157895  0.17713207 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.15058165  0.48750000 24.21052632 
-#> 
-#> [[2]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.25000000  0.75000000  0.33333333  0.20000000  0.20000000  0.89473684 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.25000000  0.25000000  0.57265552  0.54736842  0.09473684  0.21666667 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.13750000  0.53750000  9.47368421 
-#> 
-#> [[3]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>   0.4166667   0.5833333   0.1428571   0.2000000   0.2000000   0.6842105 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>   0.1666667   0.1666667   0.7388683   0.4526316  -0.1578947   0.1404762 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>   0.6655952   0.4625000   0.0000000 
-#> 
-#> [[4]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>   0.3333333   0.6666667   0.2000000   0.2000000   0.2000000   0.7894737 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>   0.2000000   0.2000000   2.1498921   0.3842105  -0.2210526   0.1307093 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>   0.9228299   0.4083333  20.0000000 
-#> 
-#> [[5]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>   0.2916667   0.7083333   0.2500000   0.2000000   0.2000000   0.8421053 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>   0.2222222   0.2222222   0.8046467   0.3684211  -0.2631579   0.1290965 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>   0.9139033   0.3958333  20.0000000 
-#> 
-#> [[6]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>   0.3333333   0.6666667   0.2000000   0.2000000   0.2000000   0.7894737 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>   0.2000000   0.2000000   0.8445633   0.4263158  -0.1789474   0.1503852 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>   1.0454257   0.4416667  20.0000000 
-#> 
-#> [[7]]
-#> ZeroOneLoss    Accuracy   Precision      Recall Sensitivity Specificity 
-#>  0.25000000  0.75000000  0.33333333  0.20000000  0.20000000  0.89473684 
-#>    F1_Score Fbeta_Score     LogLoss         AUC        Gini       PRAUC 
-#>  0.25000000  0.25000000  0.75429467  0.48421053 -0.03157895  0.17713207 
-#>     LiftAUC     GainAUC     KS_Stat 
-#>  1.15058165  0.48750000 24.21052632 
-#> 
-
-# Convert to matrix for compare performace.
-sapply(perf$performance, "c")
-#>                    [,1]       [,2]       [,3]       [,4]       [,5]       [,6]
-#> ZeroOneLoss  0.25000000 0.25000000  0.4166667  0.3333333  0.2916667  0.3333333
-#> Accuracy     0.75000000 0.75000000  0.5833333  0.6666667  0.7083333  0.6666667
-#> Precision    0.33333333 0.33333333  0.1428571  0.2000000  0.2500000  0.2000000
-#> Recall       0.20000000 0.20000000  0.2000000  0.2000000  0.2000000  0.2000000
-#> Sensitivity  0.20000000 0.20000000  0.2000000  0.2000000  0.2000000  0.2000000
-#> Specificity  0.89473684 0.89473684  0.6842105  0.7894737  0.8421053  0.7894737
-#> F1_Score     0.25000000 0.25000000  0.1666667  0.2000000  0.2222222  0.2000000
-#> Fbeta_Score  0.25000000 0.25000000  0.1666667  0.2000000  0.2222222  0.2000000
-#> LogLoss      0.76115737 0.57265552  0.7388683  2.1498921  0.8046467  0.8445633
-#> AUC          0.48421053 0.54736842  0.4526316  0.3842105  0.3684211  0.4263158
-#> Gini        -0.03157895 0.09473684 -0.1578947 -0.2210526 -0.2631579 -0.1789474
-#> PRAUC        0.17713207 0.21666667  0.1404762  0.1307093  0.1290965  0.1503852
-#> LiftAUC      1.15058165 1.13750000  0.6655952  0.9228299  0.9139033  1.0454257
-#> GainAUC      0.48750000 0.53750000  0.4625000  0.4083333  0.3958333  0.4416667
-#> KS_Stat     24.21052632 9.47368421  0.0000000 20.0000000 20.0000000 20.0000000
-#>                    [,7]
-#> ZeroOneLoss  0.25000000
-#> Accuracy     0.75000000
-#> Precision    0.33333333
-#> Recall       0.20000000
-#> Sensitivity  0.20000000
-#> Specificity  0.89473684
-#> F1_Score     0.25000000
-#> Fbeta_Score  0.25000000
-#> LogLoss      0.75429467
-#> AUC          0.48421053
-#> Gini        -0.03157895
-#> PRAUC        0.17713207
-#> LiftAUC      1.15058165
-#> GainAUC      0.48750000
-#> KS_Stat     24.21052632
-# }
-
+#>   step       model_id target is_factor positive negative fitted_model predicted 
+#>   <chr>      <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>    
+#> 1 2.Predict… logistic Kypho… TRUE      present  absent   <glm>        <prdct_cl>
+#> 2 2.Predict… rpart    Kypho… TRUE      present  absent   <rpart>      <prdct_cl>
+#> 3 2.Predict… ctree    Kypho… TRUE      present  absent   <BinaryTr>   <prdct_cl>
+#> 4 2.Predict… randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <prdct_cl>
+#> 5 2.Predict… ranger   Kypho… TRUE      present  absent   <ranger>     <prdct_cl>
+#> 6 2.Predict… xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <prdct_cl>
+#> 7 2.Predict… lasso    Kypho… TRUE      present  absent   <lognet>     <prdct_cl>
+
+# Calculate performace metrics. (Case 2)
+perf <- run_performance(pred, pull(test[, 1]))
+#> Error in purrr::map(., ~future::value(.x)): ℹ In index: 1.
+#> Caused by error:
+#> ! object 'pred' not found
+perf
+#> Error in eval(expr, envir, enclos): object 'perf' not found
+perf$performance
+#> Error in eval(expr, envir, enclos): object 'perf' not found
+
+# Convert to matrix for compare performace.
+sapply(perf$performance, "c")
+#> Error in eval(expr, envir, enclos): object 'perf' not found
+# }
+
      @@ -370,8 +222,7 @@

      Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/run_predict.html b/docs/reference/run_predict.html index 838f99c..9283464 100644 --- a/docs/reference/run_predict.html +++ b/docs/reference/run_predict.html @@ -1,5 +1,5 @@ -Predict binary classification model — run_predict • alookrPredict binary classification model — run_predict • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -59,21 +59,28 @@

    Predict binary classification model

    - 
    run_predict(model, .data, cutoff = 0.5)
    + 
    run_predict(model, .data, cutoff = 0.5)

    Arguments

    model

    A model_df. results of fitted model that created by run_models().

    + +
    .data

    A tbl_df. The data set to predict the model. It also supports tbl, and data.frame objects.

    + +
    cutoff

    numeric. Cut-off that determines the positive from the probability of predicting the positive.

    +

    Value

    -

    model_df. results of predicted model. + + +

    model_df. results of predicted model. model_df is composed of tbl_df and contains the following variables.:

    • step : character. The current stage in the model fit process. The result of calling run_predict() is returned as "2.Predicted".

    • model_id : character. Type of fit model.

    • target : character. Name of target variable.

      • @@ -99,27 +106,27 @@

      Details

      Examples

      - 
      library(dplyr)
-
-# Divide the train data set and the test data set.
-sb <- rpart::kyphosis %>%
-  split_by(Kyphosis)
-
-# Extract the train data set from original data set.
-train <- sb %>%
-  extract_set(set = "train")
-
-# Extract the test data set from original data set.
-test <- sb %>%
-  extract_set(set = "test")
-
-# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
-train <- sb %>%
-  sampling_target(seed = 1234L, method = "ubSMOTE")
-
-# Cleaning the set.
-train <- train %>%
-  cleanse
+    
      library(dplyr)
+
+# Divide the train data set and the test data set.
+sb <- rpart::kyphosis %>%
+  split_by(Kyphosis)
+
+# Extract the train data set from original data set.
+train <- sb %>%
+  extract_set(set = "train")
+
+# Extract the test data set from original data set.
+test <- sb %>%
+  extract_set(set = "test")
+
+# Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
+train <- sb %>%
+  sampling_target(seed = 1234L, method = "ubSMOTE")
+
+# Cleaning the set.
+train <- train %>%
+  cleanse
 #> ── Checking unique value ─────────────────────────── unique value is one ──
 #> No variables that unique value is one.
 #> 
@@ -130,10 +137,10 @@ 
      Examples
      
 #> No character variables.
 #> 
 #> 
-
-# Run the model fitting.
-result <- run_models(.data = train, target = "Kyphosis", positive = "present")
-result
+
+# Run the model fitting.
+result <- run_models(.data = train, target = "Kyphosis", positive = "present")
+result
 #> # A tibble: 7 × 7
 #>   step     model_id     target   is_factor positive negative fitted_model
 #>   <chr>    <chr>        <chr>    <lgl>     <chr>    <chr>    <list>      
@@ -144,35 +151,21 @@ 
      Examples
      
 #> 5 1.Fitted ranger       Kyphosis TRUE      present  absent   <ranger>    
 #> 6 1.Fitted xgboost      Kyphosis TRUE      present  absent   <xgb.Bstr>  
 #> 7 1.Fitted lasso        Kyphosis TRUE      present  absent   <lognet>    
-
-# Predict the model.
-pred <- run_predict(result, test)
-pred
-#> # A tibble: 7 × 8
-#>   step        model_id target is_factor positive negative fitted_model predicted
-#>   <chr>       <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>   
-#> 1 2.Predicted logistic Kypho… TRUE      present  absent   <glm>        <fct>    
-#> 2 2.Predicted rpart    Kypho… TRUE      present  absent   <rpart>      <fct>    
-#> 3 2.Predicted ctree    Kypho… TRUE      present  absent   <BinaryTr>   <fct>    
-#> 4 2.Predicted randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <fct>    
-#> 5 2.Predicted ranger   Kypho… TRUE      present  absent   <ranger>     <fct>    
-#> 6 2.Predicted xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <fct>    
-#> 7 2.Predicted lasso    Kypho… TRUE      present  absent   <lognet>     <fct>    
-
-# Run the several kinds model predict by dplyr
-result %>%
-  run_predict(test)
+
+# Run the several kinds model predict by dplyr
+result %>%
+  run_predict(test)
 #> # A tibble: 7 × 8
-#>   step        model_id target is_factor positive negative fitted_model predicted
-#>   <chr>       <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>   
-#> 1 2.Predicted logistic Kypho… TRUE      present  absent   <glm>        <fct>    
-#> 2 2.Predicted rpart    Kypho… TRUE      present  absent   <rpart>      <fct>    
-#> 3 2.Predicted ctree    Kypho… TRUE      present  absent   <BinaryTr>   <fct>    
-#> 4 2.Predicted randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <fct>    
-#> 5 2.Predicted ranger   Kypho… TRUE      present  absent   <ranger>     <fct>    
-#> 6 2.Predicted xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <fct>    
-#> 7 2.Predicted lasso    Kypho… TRUE      present  absent   <lognet>     <fct>    
-
+#>   step       model_id target is_factor positive negative fitted_model predicted 
+#>   <chr>      <chr>    <chr>  <lgl>     <chr>    <chr>    <list>       <list>    
+#> 1 2.Predict… logistic Kypho… TRUE      present  absent   <glm>        <prdct_cl>
+#> 2 2.Predict… rpart    Kypho… TRUE      present  absent   <rpart>      <prdct_cl>
+#> 3 2.Predict… ctree    Kypho… TRUE      present  absent   <BinaryTr>   <prdct_cl>
+#> 4 2.Predict… randomF… Kypho… TRUE      present  absent   <rndmFrs.>   <prdct_cl>
+#> 5 2.Predict… ranger   Kypho… TRUE      present  absent   <ranger>     <prdct_cl>
+#> 6 2.Predict… xgboost  Kypho… TRUE      present  absent   <xgb.Bstr>   <prdct_cl>
+#> 7 2.Predict… lasso    Kypho… TRUE      present  absent   <lognet>     <prdct_cl>
+
      @@ -187,8 +180,7 @@

      Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sampling_target.html b/docs/reference/sampling_target.html index b745e46..a6f4833 100644 --- a/docs/reference/sampling_target.html +++ b/docs/reference/sampling_target.html @@ -1,67 +1,12 @@ - - - - - - - -Extract the data to fit the model — sampling_target • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Extract the data to fit the model — sampling_target • alookr - - - - + + -
    -
    - -
    - -
    +

    Extract the data to fit the model

    - Source: R/sampling.R + Source: R/sampling.R
    @@ -120,74 +58,76 @@

    Extract the data to fit the model

    To solve the imbalanced class, perform sampling in the train set of split_df.

    - 
    sampling_target(
-  .data,
-  method = c("ubUnder", "ubOver", "ubSMOTE"),
-  seed = NULL,
-  perc = 50,
-  k = ifelse(method == "ubSMOTE", 5, 0),
-  perc.over = 200,
-  perc.under = 200
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    .data

    an object of class "split_df", usually, a result of a call to split_df().

    method

    character. sampling methods. "ubUnder" is under-sampling, -and "ubOver" is over-sampling, "ubSMOTE" is SMOTE(Synthetic Minority Over-sampling TEchnique).

    seed

    integer. random seed used for sampling

    perc

    integer. The percentage of positive class in the final dataset. -It is used only in under-sampling. The default is 50. perc can not exceed 50.

    k

    integer. It is used only in over-sampling and SMOTE. +

    + 
    sampling_target(
+  .data,
+  method = c("ubUnder", "ubOver", "ubSMOTE"),
+  seed = NULL,
+  perc = 50,
+  k = ifelse(method == "ubSMOTE", 5, 0),
+  perc.over = 200,
+  perc.under = 200
+)
    +
    + +
    +

    Arguments

    +
    .data
    +

    an object of class "split_df", usually, a result of a call to split_df().

    + + +
    method
    +

    character. sampling methods. "ubUnder" is under-sampling, +and "ubOver" is over-sampling, "ubSMOTE" is SMOTE(Synthetic Minority Over-sampling TEchnique).

    + + +
    seed
    +

    integer. random seed used for sampling

    + + +
    perc
    +

    integer. The percentage of positive class in the final dataset. +It is used only in under-sampling. The default is 50. perc can not exceed 50.

    + + +
    k
    +

    integer. It is used only in over-sampling and SMOTE. If over-sampling and if K=0: sample with replacement from the minority class until we have the same number of instances in each class. under-sampling and if K>0: sample with replacement from the minority class until we have k-times the original number of minority instances. If SMOTE, the number of neighbours to consider as the pool from where the new -examples are generated

    perc.over

    integer. It is used only in SMOTE. per.over/100 is the number of new instances -generated for each rare instance. If perc.over < 100 a single instance is generated.

    perc.under

    integer. It is used only in SMOTE. perc.under/100 is the number -of "normal" (majority class) instances that are randomly selected for each smoted -observation.

    +examples are generated

    -

    Value

    -

    An object of train_df.

    -

    Details

    +
    perc.over
    +

    integer. It is used only in SMOTE. per.over/100 is the number of new instances +generated for each rare instance. If perc.over < 100 a single instance is generated.

    + +
    perc.under
    +

    integer. It is used only in SMOTE. perc.under/100 is the number +of "normal" (majority class) instances that are randomly selected for each smoted +observation.

    + +
    +
    +

    Value

    + + +

    An object of train_df.

    +
    +
    +

    Details

    In order to solve the problem of imbalanced class, sampling is performed by under sampling, over sampling, SMOTE method.

    -

    attributes of train_df class

    - +
    +
    +

    attributes of train_df class

    The attributes of the train_df class are as follows.:

    -
      -

    • sample_seed : integer. random seed used for sampling

      • +

      • sample_seed : integer. random seed used for sampling

      • method : character. sampling methods.

      • perc : integer. perc argument value

      • k : integer. k argument value

        • @@ -197,110 +137,121 @@

        Examples

        - 
        library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
        #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
        
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-# under-sampling with random seed
-under <- sb %>%
-  sampling_target(seed = 1234L)
-
-under %>%
-  count(default)
        #> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No        233
-#> 2 Yes       233
        
-# under-sampling with random seed, and minority class frequency is 40%
-under40 <- sb %>%
-  sampling_target(seed = 1234L, perc = 40)
-
-under40 %>%
-  count(default)
        #> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No        349
-#> 2 Yes       233
        
-# over-sampling with random seed
-over <- sb %>%
-  sampling_target(method = "ubOver", seed = 1234L)
-
-over %>%
-  count(default)
        #> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No       6767
-#> 2 Yes      6767
        
-# over-sampling with random seed, and k = 10
-over10 <- sb %>%
-  sampling_target(method = "ubOver", seed = 1234L, k = 10)
-
-over10 %>%
-  count(default)
        #> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No       6767
-#> 2 Yes      2330
        
-# SMOTE with random seed
-smote <- sb %>%
-  sampling_target(method = "ubSMOTE", seed = 1234L)
-
-smote %>%
-  count(default)
        #> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No        932
-#> 2 Yes       699
        
-# SMOTE with random seed, and perc.under = 250
-smote250 <- sb %>%
-  sampling_target(method = "ubSMOTE", seed = 1234L, perc.under = 250)
-
-smote250 %>%
-  count(default)
        #> # A tibble: 2 x 2
-#>   default     n
-#>   <fct>   <int>
-#> 1 No       1165
-#> 2 Yes       699
        
-
        +
    + +
    +

    Examples

    + 
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+# under-sampling with random seed
+under <- sb %>%
+  sampling_target(seed = 1234L)
+
+under %>%
+  count(default)
+#> # A tibble: 2 × 2
+#>   default     n
+#>   <fct>   <int>
+#> 1 No       6767
+#> 2 Yes       233
+
+# under-sampling with random seed, and minority class frequency is 40%
+under40 <- sb %>%
+  sampling_target(seed = 1234L, perc = 40)
+
+under40 %>%
+  count(default)
+#> # A tibble: 2 × 2
+#>   default     n
+#>   <fct>   <int>
+#> 1 No       6767
+#> 2 Yes       233
+
+# over-sampling with random seed
+over <- sb %>%
+  sampling_target(method = "ubOver", seed = 1234L)
+
+over %>%
+  count(default)
+#> # A tibble: 2 × 2
+#>   default     n
+#>   <fct>   <int>
+#> 1 No       6767
+#> 2 Yes      6767
+
+# over-sampling with random seed, and k = 10
+over10 <- sb %>%
+  sampling_target(method = "ubOver", seed = 1234L, k = 10)
+
+over10 %>%
+  count(default)
+#> # A tibble: 2 × 2
+#>   default     n
+#>   <fct>   <int>
+#> 1 No       6767
+#> 2 Yes      2330
+
+# SMOTE with random seed
+smote <- sb %>%
+  sampling_target(method = "ubSMOTE", seed = 1234L)
+
+smote %>%
+  count(default)
+#> # A tibble: 2 × 2
+#>   default     n
+#>   <fct>   <int>
+#> 1 No        932
+#> 2 Yes       699
+
+# SMOTE with random seed, and perc.under = 250
+smote250 <- sb %>%
+  sampling_target(method = "ubSMOTE", seed = 1234L, perc.under = 250)
+
+smote250 %>%
+  count(default)
+#> # A tibble: 2 × 2
+#>   default     n
+#>   <fct>   <int>
+#> 1 No       1165
+#> 2 Yes       699
+
+
    +
    + -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/split_by.data.frame.html b/docs/reference/split_by.data.frame.html index 1116cb9..4a48567 100644 --- a/docs/reference/split_by.data.frame.html +++ b/docs/reference/split_by.data.frame.html @@ -1,67 +1,12 @@ - - - - - - - -Split Data into Train and Test Set — split_by • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Split Data into Train and Test Set — split_by • alookr - - - - + + -
    -
    - -
    - -
    +

    Split Data into Train and Test Set

    - Source: R/split.R + Source: R/split.R
    @@ -120,114 +58,117 @@

    Split Data into Train and Test Set

    The split_by() splits the data.frame or tbl_df into a train set and a test set.

    - 
    split_by(.data, ...)
-
-# S3 method for data.frame
-split_by(.data, target, ratio = 0.7, seed = NULL, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    .data

    a data.frame or a tbl_df.

    ...

    further arguments passed to or from other methods.

    target

    unquoted expression or variable name. the name of the target variable

    ratio

    numeric. the ratio of the train dataset. default is 0.7

    seed

    random seed used for splitting

    - -

    Value

    - -

    An object of split_by.

    -

    Details

    +
    + 
    split_by(.data, ...)
+
+# S3 method for data.frame
+split_by(.data, target, ratio = 0.7, seed = NULL, ...)
    +
    -

    The split_df class is created, which contains the split information and criteria to separate the training and the test set.

    -

    attributes of split_by

    +
    +

    Arguments

    +
    .data
    +

    a data.frame or a tbl_df.

    + + +
    ...
    +

    further arguments passed to or from other methods.

    + + +
    target
    +

    unquoted expression or variable name. the name of the target variable

    + + +
    ratio
    +

    numeric. the ratio of the train dataset. default is 0.7

    + +
    seed
    +

    random seed used for splitting

    + +
    +
    +

    Value

    + + +

    An object of split_by.

    +
    +
    +

    Details

    +

    The split_df class is created, which contains the split information and criteria to separate the training and the test set.

    +
    +
    +

    attributes of split_by

    The attributes of the split_df class are as follows.:

    -
      -

    • split_seed : integer. random seed used for splitting

      • +

      • split_seed : integer. random seed used for splitting

      • target : character. the name of the target variable

      • binary : logical. whether the target variable is binary class

      • minority : character. the name of the minority class

      • majority : character. the name of the majority class

      • minority_rate : numeric. the rate of the minority class

      • majority_rate : numeric. the rate of the majority class

        • -
      - - -

      Examples

      - 
      library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
      #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
      
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-sb
      #> # A tibble: 10,000 x 5
-#> # Groups:   split_flag [2]
-#>    default student balance income split_flag
-#>    <fct>   <fct>     <dbl>  <dbl> <chr>     
-#>  1 No      No         730. 44362. train     
-#>  2 No      Yes        817. 12106. train     
-#>  3 No      No        1074. 31767. train     
-#>  4 No      No         529. 35704. train     
-#>  5 No      No         786. 38463. train     
-#>  6 No      Yes        920.  7492. train     
-#>  7 No      No         826. 24905. test      
-#>  8 No      Yes        809. 17600. train     
-#>  9 No      No        1161. 37469. test      
-#> 10 No      No           0  29275. test      
-#> # … with 9,990 more rows
      
-
      +
    + +
    +

    Examples

    + 
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+sb
+#> # A tibble: 10,000 × 5
+#> # Groups:   split_flag [2]
+#>    default student balance income split_flag
+#>    <fct>   <fct>     <dbl>  <dbl> <chr>     
+#>  1 No      No         730. 44362. train     
+#>  2 No      Yes        817. 12106. train     
+#>  3 No      No        1074. 31767. train     
+#>  4 No      No         529. 35704. train     
+#>  5 No      No         786. 38463. train     
+#>  6 No      Yes        920.  7492. train     
+#>  7 No      No         826. 24905. test      
+#>  8 No      Yes        809. 17600. train     
+#>  9 No      No        1161. 37469. test      
+#> 10 No      No           0  29275. test      
+#> # ℹ 9,990 more rows
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/summary.split_df.html b/docs/reference/summary.split_df.html index 44fac97..e32bc78 100644 --- a/docs/reference/summary.split_df.html +++ b/docs/reference/summary.split_df.html @@ -1,67 +1,12 @@ - - - - - - - -Summarizing split_df information — summary.split_df • alookr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Summarizing split_df information — summary.split_df • alookr + + - - - - -
    -
    - -
    - -
    +

    Summarizing split_df information

    - Source: R/split.R + Source: R/split.R
    @@ -120,100 +58,106 @@

    Summarizing split_df information

    summary method for "split_df" class.

    - 
    # S3 method for split_df
-summary(object, ...)
    - -

    Arguments

    - - - - - - - - - - -
    object

    an object of class "split_df", usually, a result of a call to split_df().

    ...

    further arguments passed to or from other methods.

    - -

    Value

    - -

    NULL is returned. +

    + 
    # S3 method for split_df
+summary(object, ...)
    +
    + +
    +

    Arguments

    +
    object
    +

    an object of class "split_df", usually, a result of a call to split_df().

    + + +
    ...
    +

    further arguments passed to or from other methods.

    + +
    +
    +

    Value

    + + +

    NULL is returned. However, the split train set and test set information are displayed. The output information is as follows.:

    -
      -

    • Random seed

      • + + +

      • Random seed

      • Number of train sets and test sets

      • Name of target variable

      • Target variable minority class and majority class information (label and ratio)

        • -
      - -

      Details

      - +
    +
    +

    Details

    summary.split_df provides information on the number of two split data sets, minority class and majority class.

    +
    -

    Examples

    - 
    library(dplyr)
-
-# Credit Card Default Data
-head(ISLR::Default)
    #>   default student   balance    income
-#> 1      No      No  729.5265 44361.625
-#> 2      No     Yes  817.1804 12106.135
-#> 3      No      No 1073.5492 31767.139
-#> 4      No      No  529.2506 35704.494
-#> 5      No      No  785.6559 38463.496
-#> 6      No     Yes  919.5885  7491.559
    
-# Generate data for the example
-sb <- ISLR::Default %>%
-  split_by(default)
-
-sb
    #> # A tibble: 10,000 x 5
-#> # Groups:   split_flag [2]
-#>    default student balance income split_flag
-#>    <fct>   <fct>     <dbl>  <dbl> <chr>     
-#>  1 No      No         730. 44362. train     
-#>  2 No      Yes        817. 12106. train     
-#>  3 No      No        1074. 31767. test      
-#>  4 No      No         529. 35704. train     
-#>  5 No      No         786. 38463. test      
-#>  6 No      Yes        920.  7492. test      
-#>  7 No      No         826. 24905. test      
-#>  8 No      Yes        809. 17600. test      
-#>  9 No      No        1161. 37469. train     
-#> 10 No      No           0  29275. test      
-#> # … with 9,990 more rows
    summary(sb)
    #> ** Split train/test set information **
-#>  + random seed        :  93222 
-#>  + split data            
-#>     - train set count :  7000 
-#>     - test set count  :  3000 
-#>  + target variable    :  default 
-#>     - minority class  :  Yes (0.033300)
-#>     - majority class  :  No (0.966700)
    
-
    +
    +

    Examples

    + 
    library(dplyr)
+
+# Credit Card Default Data
+head(ISLR::Default)
+#>   default student   balance    income
+#> 1      No      No  729.5265 44361.625
+#> 2      No     Yes  817.1804 12106.135
+#> 3      No      No 1073.5492 31767.139
+#> 4      No      No  529.2506 35704.494
+#> 5      No      No  785.6559 38463.496
+#> 6      No     Yes  919.5885  7491.559
+
+# Generate data for the example
+sb <- ISLR::Default %>%
+  split_by(default)
+
+sb
+#> # A tibble: 10,000 × 5
+#> # Groups:   split_flag [2]
+#>    default student balance income split_flag
+#>    <fct>   <fct>     <dbl>  <dbl> <chr>     
+#>  1 No      No         730. 44362. train     
+#>  2 No      Yes        817. 12106. train     
+#>  3 No      No        1074. 31767. test      
+#>  4 No      No         529. 35704. train     
+#>  5 No      No         786. 38463. test      
+#>  6 No      Yes        920.  7492. test      
+#>  7 No      No         826. 24905. test      
+#>  8 No      Yes        809. 17600. test      
+#>  9 No      No        1161. 37469. train     
+#> 10 No      No           0  29275. test      
+#> # ℹ 9,990 more rows
+summary(sb)
+#> ** Split train/test set information **
+#>  + random seed        :  93222 
+#>  + split data            
+#>     - train set count :  7000 
+#>     - test set count  :  3000 
+#>  + target variable    :  default 
+#>     - minority class  :  Yes (0.033300)
+#>     - majority class  :  No (0.966700)
+
+
    +
    +
    -
    -
    -

    Developed by Choonghyun Ryu.

    +
    +

    Developed by Choonghyun Ryu.

    -

    Site built with pkgdown 1.5.1.

    +

    Site built with pkgdown 2.0.7.

    -
    -
    +
    - - + + diff --git a/docs/reference/treatment_corr.html b/docs/reference/treatment_corr.html index 586ed15..9690ed0 100644 --- a/docs/reference/treatment_corr.html +++ b/docs/reference/treatment_corr.html @@ -1,5 +1,5 @@ -Diagnosis and removal of highly correlated variables — treatment_corr • alookrDiagnosis and removal of highly correlated variables — treatment_corr • alookr @@ -17,7 +17,7 @@ alookr - 0.3.7 + 0.3.9 @@ -59,23 +59,32 @@

    Diagnosis and removal of highly correlated variables

    - 
    treatment_corr(.data, corr_thres = 0.8, treat = TRUE, verbose = TRUE)
    + 
    treatment_corr(.data, corr_thres = 0.8, treat = TRUE, verbose = TRUE)

    Arguments

    .data

    a data.frame or a tbl_df.

    + +
    corr_thres

    numeric. Set a threshold to detecting variables when correlation greater then threshold.

    + +
    treat

    logical. Set whether to removing variables

    + +
    verbose

    logical. Set whether to echo information to the console at runtime.

    +

    Value

    -

    An object of data.frame or train_df. and return value is an object of the same type as the .data argument. However, several variables can be excluded by correlation between variables.

    + + +

    An object of data.frame or train_df. and return value is an object of the same type as the .data argument. However, several variables can be excluded by correlation between variables.

    Details

    @@ -84,24 +93,24 @@

    Details

    Examples

    - 
    # numerical variable
-x1 <- 1:100
-set.seed(12L)
-x2 <- sample(1:3, size = 100, replace = TRUE) * x1 + rnorm(1)
-set.seed(1234L)
-x3 <- sample(1:2, size = 100, replace = TRUE) * x1 + rnorm(1)
-
-# categorical variable
-x4 <- factor(rep(letters[1:20], time = 5))
-set.seed(100L)
-x5 <- factor(rep(letters[1:20 + sample(1:6, size = 20, replace = TRUE)], time = 5))
-set.seed(200L)
-x6 <- factor(rep(letters[1:20 + sample(1:3, size = 20, replace = TRUE)], time = 5))
-set.seed(300L)
-x7 <- factor(sample(letters[1:5], size = 100, replace = TRUE))
-
-exam <- data.frame(x1, x2, x3, x4, x5, x6, x7)
-str(exam)
+    
    # numerical variable
+x1 <- 1:100
+set.seed(12L)
+x2 <- sample(1:3, size = 100, replace = TRUE) * x1 + rnorm(1)
+set.seed(1234L)
+x3 <- sample(1:2, size = 100, replace = TRUE) * x1 + rnorm(1)
+
+# categorical variable
+x4 <- factor(rep(letters[1:20], time = 5))
+set.seed(100L)
+x5 <- factor(rep(letters[1:20 + sample(1:6, size = 20, replace = TRUE)], time = 5))
+set.seed(200L)
+x6 <- factor(rep(letters[1:20 + sample(1:3, size = 20, replace = TRUE)], time = 5))
+set.seed(300L)
+x7 <- factor(sample(letters[1:5], size = 100, replace = TRUE))
+
+exam <- data.frame(x1, x2, x3, x4, x5, x6, x7)
+str(exam)
 #> 'data.frame':	100 obs. of  7 variables:
 #>  $ x1: int  1 2 3 4 5 6 7 8 9 10 ...
 #>  $ x2: num  2.55 4.55 9.55 12.55 10.55 ...
@@ -110,7 +119,7 @@ 
    Examples
    
 #>  $ x5: Factor w/ 13 levels "c","e","f","g",..: 1 5 3 2 4 7 6 8 9 8 ...
 #>  $ x6: Factor w/ 15 levels "c","d","f","g",..: 1 2 3 4 3 5 6 7 8 9 ...
 #>  $ x7: Factor w/ 5 levels "a","b","c","d",..: 2 2 1 4 5 1 4 3 1 5 ...
-head(exam)
+head(exam)
 #>   x1        x2         x3 x4 x5 x6 x7
 #> 1  1  2.554297  0.1939687  a  c  c  b
 #> 2  2  4.554297  2.1939687  b  h  d  b
@@ -118,9 +127,9 @@ 
    Examples
    
 #> 4  4 12.554297  6.1939687  d  e  g  d
 #> 5  5 10.554297  3.1939687  e  g  f  e
 #> 6  6  6.554297 10.1939687  f  l  h  a
-
-# default case
-treatment_corr(exam)
+
+# default case
+treatment_corr(exam)
 #> * remove variables whose strong correlation (pearson >= 0.8)
 #>  - remove x1 : with x3 (0.825)
 #> * remove variables whose strong correlation (spearman >= 0.8)
@@ -228,25 +237,25 @@ 
    Examples
    
 #> 98  294.554297  96.1939687  t  e
 #> 99  198.554297 196.1939687  v  d
 #> 100 100.554297 198.1939687  v  a
-
-# not removing variables
-treatment_corr(exam, treat = FALSE)
+
+# not removing variables
+treatment_corr(exam, treat = FALSE)
 #> * remove variables whose strong correlation (pearson >= 0.8)
 #>  - remove x1 : with x3 (0.825)
 #> * remove variables whose strong correlation (spearman >= 0.8)
 #>  - remove x4 : with x5 (0.9649)
 #>  - remove x4 : with x6 (0.9928)
 #>  - remove x5 : with x6 (0.9485)
-
-# Set a threshold to detecting variables when correlation greater then 0.9
-treatment_corr(exam, corr_thres = 0.9, treat = FALSE)
+
+# Set a threshold to detecting variables when correlation greater then 0.9
+treatment_corr(exam, corr_thres = 0.9, treat = FALSE)
 #> * remove variables whose strong correlation (spearman >= 0.9)
 #>  - remove x4 : with x5 (0.9649)
 #>  - remove x4 : with x6 (0.9928)
 #>  - remove x5 : with x6 (0.9485)
-
-# not verbose mode
-treatment_corr(exam, verbose = FALSE)
+
+# not verbose mode
+treatment_corr(exam, verbose = FALSE)
 #>             x2          x3 x6 x7
 #> 1     2.554297   0.1939687  c  b
 #> 2     4.554297   2.1939687  d  b
@@ -348,7 +357,7 @@ 
    Examples
    
 #> 98  294.554297  96.1939687  t  e
 #> 99  198.554297 196.1939687  v  d
 #> 100 100.554297 198.1939687  v  a
-
+
    @@ -363,8 +372,7 @@

    Examples

    -

    Site built with pkgdown -2.0.2.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/sitemap.xml b/docs/sitemap.xml new file mode 100644 index 0000000..3f70453 --- /dev/null +++ b/docs/sitemap.xml @@ -0,0 +1,93 @@ + + + + /404.html + + + /LICENSE-text.html + + + /articles/cleansing.html + + + /articles/index.html + + + /articles/introduce.html + + + /articles/modeling.html + + + /articles/split.html + + + /authors.html + + + /index.html + + + /news/index.html + + + /reference/cleanse.data.frame.html + + + /reference/cleanse.split_df.html + + + /reference/compare_diag.html + + + /reference/compare_performance.html + + + /reference/compare_plot.html + + + /reference/compare_target_category.html + + + /reference/compare_target_numeric.html + + + /reference/extract_set.html + + + /reference/index.html + + + /reference/matthews.html + + + /reference/performance_metric.html + + + /reference/plot_cutoff.html + + + /reference/plot_performance.html + + + /reference/run_models.html + + + /reference/run_performance.html + + + /reference/run_predict.html + + + /reference/sampling_target.html + + + /reference/split_by.data.frame.html + + + /reference/summary.split_df.html + + + /reference/treatment_corr.html + +